Browsing by Author "Jonkman, Mirjam"

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A Comparative Study of Different Machine Learning Tools in Detecting Diabetes
(Scopus, 2021) Ghosh, Pronab; Azam, , Sami; Karim, Asif; Hassan, Mehedi; Roy, Kuber; Jonkman, Mirjam
A significant proportion of people around the world are currently suffering from the harmful effects of diabetes and a considerable number of them not being identified at an early stage. Over time this may result in serious health problem such as blindness and kidney failure. To accurately classify the disease, different machine learning (ML) approaches can be utilized. In this context, four separate ML algorithms, namely Gradient Boosting (GB), Support Vector Machine (SVM) AdaBoost (AB), and Random Forest (RF) are evaluated using the Pima Indians diabetes dataset, first with based on all features, then to the features selected with the Minimal Redundancy Maximal Relevance (MRMR) Feature Selection (FS) approach. Seven different types of performance evaluation metrics were computed with a 10-fold cross-validation (CV) approach. Computational complexity is also evaluated. The best results were obtained with the Random Forest approach, achieving an accuracy of 99.35%.
A Comparative Study of Different Machine Learning Tools in Detecting Diabetes
(Scopus, 2021) Ghosh, Pronab; Azam, Sami; Karim, Asif; Hassan, Mehedi; Roy, Kuber; Jonkman, Mirjam
A significant proportion of people around the world are currently suffering from the harmful effects of diabetes and a considerable number of them not being identified at an early stage. Over time this may result in serious health problem such as blindness and kidney failure. To accurately classify the disease, different machine learning (ML) approaches can be utilized. In this context, four separate ML algorithms, namely Gradient Boosting (GB), Support Vector Machine (SVM) AdaBoost (AB), and Random Forest (RF) are evaluated using the Pima Indians diabetes dataset, first with based on all features, then to the features selected with the Minimal Redundancy Maximal Relevance (MRMR) Feature Selection (FS) approach. Seven different types of performance evaluation metrics were computed with a 10-fold cross-validation (CV) approach. Computational complexity is also evaluated. The best results were obtained with the Random Forest approach, achieving an accuracy of 99.35%.
A Comprehensive Unsupervised Framework for Chronic Kidney Disease Prediction
(Scopus, 2021) Antony, Linta; Azam, Sami; Ignatious, Eva; Quadir, Ryana; Beeravolu, Abhijith Reddy; Jonkman, Mirjam; De Boer, Friso
The incidence, prevalence, and progression of chronic kidney disease (CKD) conditions have evolved over time, especially in countries that have varied social determinants of health. In most countries, diabetics and hypertension are the main causes of CKDs. The global guidelines classify CKD as a condition that results in decreased kidney function over time, as indicated by glomerular filtration rate (GFR) and markers of kidney damage. People with CKDs are likely to die at an early age. It is crucial for doctors to diagnose various conditions associated with CKD in an early stage because early detection may prevent or even reverse kidney damage. Early detection can provide better treatment and proper care to the patients. In many regional hospital/clinics, there is a shortage of nephrologists or general medical persons who diagnose the symptoms. This has resulted in patients waiting longer to get a diagnosis. Therefore, this research believes developing an intelligent system to classify a patient into classes of `CKD' or `Non-CKD' can help the doctors to deal with multiple patients and provide diagnosis faster. In time, organizations can implement the proposed machine learning framework in regional clinics that have lower medical expert retention, this can provide early diagnosis to patients in regional areas. Although, several researchers have tried to address the situation by developing intelligent systems using supervised machine learning methods, till date limited studies have used unsupervised machine learning algorithms. The primary aim of this research is to implement and compare the performance of various unsupervised algorithms and identify best possible combinations that can provide better accuracy and detection rate. This research has implemented five unsupervised algorithms, K-Means Clustering, DB-Scan, I-Forest, and Autoencoder. And integrating them with various feature selection methods. Integrating feature reduction methods with K-Means Clustering algo...
A Computer-Aided Diagnostic System to Identify Diabetic Retinopathy, Utilizing a Modified Compact Convolutional Transformer and Low-Resolution Images to Reduce Computation Time
(MDPI, 2023-05-28) Khan, Inam Ullah; Raiaan, Mohaimenul Azam Khan; Fatema, Kaniz; Azam, Sami; Rashid, Rafi Ur; Mukta, Saddam Hossain; Jonkman, Mirjam; Boer, Friso De
Diabetic retinopathy (DR) is the foremost cause of blindness in people with diabetes worldwide, and early diagnosis is essential for effective treatment. Unfortunately, the present DR screening method requires the skill of ophthalmologists and is time-consuming. In this study, we present an automated system for DR severity classification employing the fine-tuned Compact Convolutional Transformer (CCT) model to overcome these issues. We assembled five datasets to generate a more extensive dataset containing 53,185 raw images. Various image pre-processing techniques and 12 types of augmentation procedures were applied to improve image quality and create a massive dataset. A new DR-CCTNet model is proposed. It is a modification of the original CCT model to address training time concerns and work with a large amount of data. Our proposed model delivers excellent accuracy even with low-pixel images and still has strong performance with fewer images, indicating that the model is robust. We compare our model’s performance with transfer learning models such as VGG19, VGG16, MobileNetV2, and ResNet50. The test accuracy of the VGG19, ResNet50, VGG16, and MobileNetV2 were, respectively, 72.88%, 76.67%, 73.22%, and 71.98%. Our proposed DR-CCTNet model to classify DR outperformed all of these with a 90.17% test accuracy. This approach provides a novel and efficient method for the detection of DR, which may lower the burden on ophthalmologists and expedite treatment for patients.
A Framework to Address Security Concerns in Three Layers of IoT
(IEEE, 2020-11-20) Jose, Alwyn; Azam, Sami; Karim, Asif; Shanmugam, Bharanidharan; Faisal, Fahad; Islam, Ashraful; De Boer, Friso; Jonkman, Mirjam
The Internet of Things (IoT) is becoming part of many aspects of our life, including healthcare, home utilities, retail, energy, logistics, etc. This prolific and ubiquitous nature of IoT based systems brings with it the threats of cyber-attacks in a variety of forms. An IoT framework is a set of controlling rules, standards and protocols which makes implementation of IoT applications somewhat streamlined. However, due to the existence of a plethora of IoT devices, applications and technologies, standardization of IoT protocols is a complex undertaking. Several well-known IT organizations have their own customized standards for the IoT platform. However, the lack of stable standardization has been a prime concern for quite some time. This research outlines the overall technologies used in IoT security implementation and an overview of different threats faced by IoT devices. The work also recommends a security framework that can effectively be implemented with various IoT based systems.
A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images
(IEEE, 2023-05-01) Raiaan, Mohaimenul Azam Khan; Fatema, Kaniz; Khan, Inam Ullah; Azam, Sami; Rashid, Md. Rafi Ur; Mukta, Md. Saddam Hossain; Jonkman, Mirjam
Diabetic retinopathy (DR) is a common complication of diabetes mellitus, and retinal blood vessel damage can lead to vision loss and blindness if not recognized at an early stage. Manual DR detection using large fundus image data is time-consuming and error-prone. An effective automatic DR detection system can be significantly faster and potentially more accurate. This study aims to classify fundus images into five DR classes, using deep learning methods, with the highest possible accuracy and the lowest possible computational time. Three distinct DR datasets, APTOS, Messidor2, and IDRiD, are merged, resulting in 5,819 raw images. Before training the model, various image preprocessing techniques are applied to remove artifacts and noise from the images and improve their quality. Three augmentation techniques: geometric, photometric, and elastic deformation, are used to create a balanced dataset. A shallow convolutional neural network (CNN) is developed using three blocks of convolutional layers and maxpool layers with a categorical cross-entropy loss function, Adam optimizer, 0.0001 learning rate, and 64 batch size as a base model, and this is also employed to determine the best data augmentation method for further processing. A study to optimize the performance is then conducted by changing different components and hyperparameters of the base model, resulting in our proposed RetNet-10 model. Six cutting-edge models are employed for comparison. Our proposed RetNet-10 model performed the best, with a testing accuracy of 98.65%. MobileNetV2, VGG16, Xception, VGG19, InceptionV3 and ResNet50 achieved testing accuracies of 91.42%, 90.16%,89.57%, 88.21%, 87.68% and 87.23%, respectively. The model is also trained with several k values to assess its robustness. After image processing and data augmentation, using the combined dataset, and fine-tuning the base model, our proposed RetNet-10 model outperformed other automated methods for DR diagnosis.
A Novel Data and Model Centric Artificial Intelligence Based Approach in Developing High-Performance Named Entity Recognition for Bengali Language
(PLOS ONE, 2023-09-22) Lima, Khadija Akter; Hasib, Khan Md; Azam, Sami; Karim, Asif; Montaha, Sidratul; Noori, Sheak Rashed Haider; Jonkman, Mirjam
Named Entity Recognition (NER) plays a significant role in enhancing the performance of all types of domain specific applications in Natural Language Processing (NLP). According to the type of application, the goal of NER is to identify target entities based on the context of other existing entities in a sentence. Numerous architectures have demonstrated good performance for high-resource languages such as English and Chinese NER. However, currently existing NER models for Bengali could not achieve reliable accuracy due to morphological richness of Bengali and limited availability of resources. This work integrates both Data and Model Centric AI concepts to achieve a state-of-the-art performance. A unique dataset was created for this study demonstrating the impact of a good quality dataset on accuracy. We proposed a method for developing a high quality NER dataset for any language. We have used our dataset to evaluate the performance of various Deep Learning models. A hybrid model performed with the exact match F1 score of 87.50%, partial match F1 score of 92.31%, and micro F1 score of 98.32%. Our proposed model reduces the need for feature engineering and utilizes minimal resources.
A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer
(2021 International Joint Conference on Neural Networks (IJCNN), IEEE, 2021-09-21) Ghosh, Pronab; Azam, Sami; Hasib, Khan Md.; Karim, Asif; Jonkman, Mirjam; Anwar, Adnan
Breast Cancer is one of the leading causes of death worldwide. Early detection is very important in increasing survival rates. Intensive research is therefore done to improve early detection of such cancers through the use of available technology. This includes various image processing techniques andgeneral machine learning. However, the reported accuracy for many of these studies was often not at the desirable level. Deep Learning based techniques are a promising approach for the early detection of Breast Cancer. We have therefore done a comparative analysis of seven Deep Learning techniques applied to the Wisconsin Breast Cancer (Diagnostic) Dataset. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were proven to be the most effective algorithms as these have demonstrated good results for the majority of performance indicators used in this study, including an accuracy of over 99 percent.
A Robust Framework Combining Image Processing and Deep Learning Hybrid Model to Classify Cardiovascular Diseases Using a Limited Number of Paper-Based Complex ECG Images
(Scopus, 22-11-07) Fatema, Kaniz; Montaha, Sidratul; Rony, Md. Awlad Hossen; Azam, Sami; Hasan, Md. Zahid; Jonkman, Mirjam
Heart disease can be life-threatening if not detected and treated at an early stage. The electrocardiogram (ECG) plays a vital role in classifying cardiovascular diseases, and often physicians and medical researchers examine paper-based ECG images for cardiac diagnosis. An automated heart disease prediction system might help to classify heart diseases accurately at an early stage. This study aims to classify cardiac diseases into five classes with paper-based ECG images using a deep learning approach with the highest possible accuracy and the lowest possible time complexity. This research consists of two approaches. In the first approach, five deep learning models, InceptionV3, ResNet50, MobileNetV2, VGG19, and DenseNet201, are employed. In the second approach, an integrated deep learning model (InRes-106) is introduced, combining InceptionV3 and ResNet50. This model is developed as a deep convolutional neural network capable of extracting hidden and high-level features from images. An ablation study is conducted on the proposed model altering several components and hyper parameters, improving the performance even further. Before training the model, several image pre-processing techniques are employed to remove artifacts and enhance the image quality. Our proposed hybrid InRes-106 model performed best with a testing accuracy of 98.34%. The InceptionV3 model acquired a testing accuracy of 90.56%, the ResNet50 89.63%, the DenseNet201 88.94%, the VGG19 87.87%, and the MobileNetV2 achieved 80.56% testing accuracy. The model is trained with a k-fold cross-validation technique with different k values to evaluate the robustness further. Although the dataset contains a limited number of complex ECG images, our proposed approach, based on various image pre-processing techniques, model fine-tuning, and ablation studies, can effectively diagnose cardiac diseases.
A Shallow Deep Learning Approach To Classify Skin Cancer Using Down-Scaling Method To Minimize Time and Space Complexity
(Daffodil International University, 2022-08-04) Sidratul, Montaha; Azam, Sami; Rafid, A. K. M. Rakibul Haque; Islam, Sayma; Ghosh, Pronab; Jonkman, Mirjam
The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting over fitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the ‘box blur’ down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted. This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a light weighted network using a limited amount of training data.
An Effective Approach To Address Processing Time and Computational Complexity Employing Modified CCT for Lung Disease Classification
(Scopus, 22-11-22) Khan, Inam Ullah; Azam, Sami; Montaha, Sidratul; Mahmud, Abdullah Al; Rafid, A.K.M. Rakibul Haque; Hasan, Md. Zahid; Jonkman, Mirjam
Early identification and adequate treatment can help prevent lung disorders from becoming chronic, severe, and life-threatening. X-ray images are commonly used and an automated and effective method involving deep learning techniques can potentially contribute to quick and accurate diagnosis of lung disorders. However, in the study of medical imaging using deep learning, two obstacles limit interpretability. One is an insufficient and imbalanced number of training samples in most medical datasets. The other is excessive training time. Although training time can be reduced by decreasing the number of pixels in the images, training with low resolution images tends to result in poor performance. This study represents a solution to overcome these impediments by balancing the number of images and reducing overall processing time while preserving accuracy. The dataset used in this research contains an unequal number of images in the different classes. The quantity of data in the classes is balanced by creating synthetic images based on the patterns and characteristics of the original images, using a Deep Convolutional Generative Adversarial Network (DCGAN). Unwanted regions are removed from the X-ray images, the brightness and contrast of the images are enhanced, and the abnormalities are highlighted by using different artifact removal, noise reduction, and enhancement techniques. We propose a Modified Compact Convolutional Transformer (MCCT) model using 32 × 32 sized images for the categorization of lung disorders into four classes. An ablation study of eleven cases is employed to adjust several hyper parameters and layer topologies. This reduces training time while preserving accuracy. Six transfer learning models, VGG19, VGG16, ResNet152, ResNet50, ResNet50V2, and MobileNet are applied with the same image size the performance is compared with the proposed MCCT model. Our MCCT model records the greatest test accuracy of 95.37%, requiring a short training time, 10-12 s/epoch, whereas the other models only reach near-moderate performance with accuracies ranging from 43% to 79% and training times of 80-90 s/epoch. The robustness of the model with regards to the number of training samples is validated by training the model multiple times reducing the number of training images gradually from 49621 images to 6204 images. Results suggest that even with a smaller dataset, the performance is sustained. Our proposed approach may contribute to an effective CAD based diagnostic system by addressing the issues of insufficient and imbalanced numbers of medical images, excessive training times and low-resolution images.
Automated Breast Tumor Ultrasound Image Segmentation With Hybrid UNet and Classification Using Fine-Tuned CNN Model
(Elsevier, 2023-10-20) Hossain, Shahed; Azam, Sami; Montaha, Sidratul; Karim, Asif; Chowa, Sadia Sultana; Mondol, Chaity; Hasan, Md Zahid; Jonkman, Mirjam
Introduction Breast cancer stands as the second most deadly form of cancer among women worldwide. Early diagnosis and treatment can significantly mitigate mortality rates. Purpose The study aims to classify breast ultrasound images into benign and malignant tumors. This approach involves segmenting the breast's region of interest (ROI) employing an optimized UNet architecture and classifying the ROIs through an optimized shallow CNN model utilizing an ablation study. Method Several image processing techniques are utilized to improve image quality by removing text, artifacts, and speckle noise, and statistical analysis is done to check the enhanced image quality is satisfactory. With the processed dataset, the segmentation of breast tumor ROI is carried out, optimizing the UNet model through an ablation study where the architectural configuration and hyperparameters are altered. After obtaining the tumor ROIs from the fine-tuned UNet model (RKO-UNet), an optimized CNN model is employed to classify the tumor into benign and malignant classes. To enhance the CNN model's performance, an ablation study is conducted, coupled with the integration of an attention unit. The model's performance is further assessed by classifying breast cancer with mammogram images. Result The proposed classification model (RKONet-13) results in an accuracy of 98.41 %. The performance of the proposed model is further compared with five transfer learning models for both pre-segmented and post-segmented datasets. K-fold cross-validation is done to assess the proposed RKONet-13 model's performance stability. Furthermore, the performance of the proposed model is compared with previous literature, where the proposed model outperforms existing methods, demonstrating its effectiveness in breast cancer diagnosis. Lastly, the model demonstrates its robustness for breast cancer classification, delivering an exceptional performance of 96.21 % on a mammogram dataset. Conclusion The efficacy of this study relies on image pre-processing, segmentation with hybrid attention UNet, and classification with fine-tuned robust CNN model. This comprehensive approach aims to determine an effective technique for detecting breast cancer within ultrasound images.
Automated Detection of Broncho-Arterial Pairs Using CT Scans Employing Different Approaches To Classify Lung Diseases
(MDPI Publications, 2023-01-05) Azam, Sami; Rafid, A.K.M. Rakibul Haque; Montaha, Sidratul; Karim, Asif; Jonkman, Mirjam; Boer, Friso De
Current research indicates that for the identification of lung disorders, comprising pneumonia and COVID-19, structural distortions of bronchi and arteries (BA) should be taken into account. CT scans are an effective modality to detect lung anomalies. However, anomalies in bronchi and arteries can be difficult to detect. Therefore, in this study, alterations of bronchi and arteries are considered in the classification of lung diseases. Four approaches to highlight these are introduced: (a) a Hessian-based approach, (b) a region-growing algorithm, (c) a clustering-based approach, and (d) a color-coding-based approach. Prior to this, the lungs are segmented, employing several image preprocessing algorithms. The utilized COVID-19 Lung CT scan dataset contains three classes named Non-COVID, COVID, and community-acquired pneumonia, having 6983, 7593, and 2618 samples, respectively. To classify the CT scans into three classes, two deep learning architectures, (a) a convolutional neural network (CNN) and (b) a CNN with long short-term memory (LSTM) and an attention mechanism, are considered. Both these models are trained with the four datasets achieved from the four approaches. Results show that the CNN model achieved test accuracies of 88.52%, 87.14%, 92.36%, and 95.84% for the Hessian, the region-growing, the color-coding, and the clustering-based approaches, respectively. The CNN with LSTM and an attention mechanism model results in an increase in overall accuracy for all approaches with an 89.61%, 88.28%, 94.61%, and 97.12% test accuracy for the Hessian, region-growing, color-coding, and clustering-based approaches, respectively. To assess overfitting, the accuracy and loss curves and k-fold cross-validation technique are employed. The Hessian-based and region-growing algorithm-based approaches produced nearly equivalent outcomes. Our proposed method outperforms state-of-the-art studies, indicating that it may be worthwhile to pay more attention to BA features in lung disease classification based on CT images.
Breastnet18
(Biology, 2021-11-13) Montaha, Sidratul; Azam, Sami; Muhammad Rakibul Haque Rafid, Abul Kalam; Ghosh, Pronab; Hasan, Md. Zahid; Jonkman, Mirjam; De Boer, Friso
Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible over fitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.
Development of an Automated Optimal Distance Feature-Based Decision System for Diagnosing Knee Osteoarthritis Using Segmented X-Ray Images
(Elsevier, 2023-11-03) Fatema, Kaniz; Rony, Md Awlad Hossen; Azam, Sami; Mukta, Md Saddam Hossain; Karim, Asif; Hasan, Md Zahid; Jonkman, Mirjam
Knee Osteoarthritis (KOA) is a leading cause of disability and physical inactivity. It is a degenerative joint disease that affects the cartilage, cushions the bones, and protects them from rubbing against each other during motion. If not treated early, it may lead to knee replacement. In this regard, early diagnosis of KOA is necessary for better treatment. Nevertheless, manual KOA detection is time-consuming and error-prone for large data hubs. In contrast, an automated detection system aids the specialist in diagnosing KOA grades accurately and quickly. So, the main objective of this study is to create an automated decision system that can analyze KOA and classify the severity grades, utilizing the extracted features from segmented X-ray images. In this study, two different datasets were collected from the Mendeley and Kaggle database and combined to generate a large data hub containing five classes: Grade 0 (Healthy), Grade 1 (Doubtful), Grade 2 (Minimal), Grade 3 (Moderate), and Grade 4 (Severe). Several image processing techniques were employed to segment the region of interest (ROI). These included Gradient-weighted Class Activation Mapping (Grad-Cam) to detect the ROI, cropping the ROI portion, applying histogram equalization (HE) to improve contrast, brightness, and image quality, and noise reduction (using Otsu thresholding, inverting the image, and morphological closing). Besides, the focus filtering method was utilized to eliminate unwanted images. Then, six feature sets (morphological, GLCM, statistical, texture, LBP, and proposed features) were generated from segmented ROIs. After evaluating the statistical significance of the features and selection methods, the optimal feature set (prominent six distance features) was selected, and five machine learning (ML) models were employed. Additionally, a decision-making strategy based on the six optimal features is proposed. The XGB model outperformed other models with a 99.46 % accuracy, using six distance features, and the proposed decision-making strategy was validated by testing 30 images.
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques
(IEEE, 2021-01-22) Ghosh, Pronab; Azam, Sami; Jonkman, Mirjam; Karim, Asif; Shamrat, F. M. Javed Mehedi; Ignatious, Eva; Shultana, Shahana
Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and Lasso Feature Selection Techniques
(Scopus, 2021) Ghosh, Pronab; Azam, Sami; Jonkman, Mirjam; Karim, Asif; Shamrat, F. M. Javed Mehedi
Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).
Graph Neural Network-Based Breast Cancer Diagnosis Using Ultrasound Images With Optimized Graph Construction Integrating the Medically Significant Features
(MDPI Publications, 2023-11-20) Chowa, Sadia Sultana; Azam, Sami; Montaha, Sidratul; Payel, Israt Jahan; Bhuiyan, Md Rahad Islam; Hasan, Md. Zahid; Jonkman, Mirjam
Purpose An automated computerized approach can aid radiologists in the early diagnosis of breast cancer. In this study, a novel method is proposed for classifying breast tumors into benign and malignant, based on the ultrasound images through a Graph Neural Network (GNN) model utilizing clinically significant features. Method Ten informative features are extracted from the region of interest (ROI), based on the radiologists’ diagnosis mark- ers. The significance of the features is evaluated using density plot and T test statistical analysis method. A feature table is generated where each row represents individual image, considered as node, and the edges between the nodes are denoted by calculating the Spearman correlation coefficient. A graph dataset is generated and fed into the GNN model. The model is configured through ablation study and Bayesian optimization. The optimized model is then evaluated with different cor- relation thresholds for getting the highest performance with a shallow graph. The performance consistency is validated with k-fold cross validation. The impact of utilizing ROIs and handcrafted features for breast tumor classification is evaluated by comparing the model’s performance with Histogram of Oriented Gradients (HOG) descriptor features from the entire ultrasound image. Lastly, a clustering-based analysis is performed to generate a new filtered graph, considering weak and strong relationships of the nodes, based on the similarities. Results The results indicate that with a threshold value of 0.95, the GNN model achieves the highest test accuracy of 99.48%, precision and recall of 100%, and F1 score of 99.28%, reducing the number of edges by 85.5%. The GNN model’s perfor- mance is 86.91%, considering no threshold value for the graph generated from HOG descriptor features. Different threshold values for the Spearman’s correlation score are experimented with and the performance is compared. No significant differ- ences are observed between the previous graph and the filtered graph. Conclusion The proposed approach might aid the radiologists in effective diagnosing and learning tumor pattern of breast cancer.
MCNN-LSTM: Combining CNN and LSTM to Classify Multi-Class Text in Imbalanced News Data
(IEEE, 2023-08-29) Hasib, Khan Md.; Azam, Sami; Karim, Asif; Marouf, Ahmed Al; Shamrat, F M Javed Mehedi; Montaha, Sidratul; Yeo, Kheng Cher; Jonkman, Mirjam
"Searching, retrieving, and arranging text in ever-larger document collections necessitate more efficient information processing algorithms. Document categorization is a crucial component of various information processing systems for supervised learning. As the quantity of documents grows, the performance of classic supervised classifiers has deteriorated because of the number of document categories. Assigning documents to a predetermined set of classes is called text classification. It is utilized extensively in a wide range of data-intensive applications. However, the fact that real-world implementations of these models are plagued with shortcomings begs for more investigation. Imbalanced datasets hinder the most prevalent high-performance algorithms. In this paper, we propose an approach name multi-class Convolutional Neural Network (MCNN)-Long Short-Time Memory (LSTM), which combines two deep learning techniques, Convolutional Neural Network (CNN) and Long Short-Time Memory, for text classification in news data. CNN’s are used as feature extractors for the LSTMs on text input data and have the spatial structure of words in a sentence, paragraph, or document. The dataset is also imbalanced, and we use the Tomek-Link algorithm to balance the dataset and then apply our model, which shows better performance in terms of F1- score (98%) and Accuracy (99.71%) than the existing works. The combination of deep learning techniques used in our approach is ideal for the classification of imbalanced datasets with underrepresented categories. Hence, our method outperformed other machine learning algorithms in text classification by a large margin. We also compare our results with traditional machine learning algorithms in terms of imbalanced and balanced datasets."
MNet-10
(Daffodil International University, 2022-08-22) Montaha, Sidratul; Azam, Sami; Rafid, A. K. M. Rakibul Haque; Hasan, Md. Zahid; Karim, Asif; Hasib, Khan Md.; Patel, Shobhit K.; Jonkman, Mirjam; Mannan, Zubaer Ibna
Interpretation of medical images with a computer-aided diagnosis (CAD) system is arduous because of the complex structure of cancerous lesions in different imaging modalities, high degree of resemblance between inter-classes, presence of dissimilar characteristics in intra-classes, scarcity of medical data, and presence of artifacts and noises. In this study, these challenges are addressed by developing a shallow convolutional neural network (CNN) model with optimal configuration performing ablation study by altering layer structure and hyper-parameters and utilizing a suitable augmentation technique. Eight medical datasets with different modalities are investigated where the proposed model, named MNet-10, with low computational complexity is able to yield optimal performance across all datasets. The impact of photometric and geometric augmentation techniques on different datasets is also evaluated. We selected the mammogram dataset to proceed with the ablation study for being one of the most challenging imaging modalities. Before generating the model, the dataset is augmented using the two approaches. A base CNN model is constructed first and applied to both the augmented and non-augmented mammogram datasets where the highest accuracy is obtained with the photometric dataset. Therefore, the architecture and hyper-parameters of the model are determined by performing an ablation study on the base model using the mammogram photometric dataset. Afterward, the robustness of the network and the impact of different augmentation techniques are assessed by training the model with the rest of the seven datasets. We obtain a test accuracy of 97.34% on the mammogram, 98.43% on the skin cancer, 99.54% on the brain tumor magnetic resonance imaging (MRI), 97.29% on the COVID chest X-ray, 96.31% on the tympanic membrane, 99.82% on the chest computed tomography (CT) scan, and 98.75% on the breast cancer ultrasound datasets by photometric augmentation and 96.76% on the breast cancer microscopic biopsy dataset by geometric augmentation. Moreover, some elastic deformation augmentation methods are explored with the proposed model using all the datasets to evaluate their effectiveness. Finally, VGG16, InceptionV3, and ResNet50 were trained on the best-performing augmented datasets, and their performance consistency was compared with that of the MNet-10 model. The findings may aid future researchers in medical data analysis involving ablation studies and augmentation techniques.