Browsing by Author "Karim, Asif"

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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 Framework to Address Security Concerns in Three Layers of IoT
(IEEE, 2020-11) Jose, Alwyn; Azam, Sami; Karim, Asif; Shanmugam, Bharanidharan; Faisal, Fahad; Islam, Ashraful; De Boer, Friso; Jonkma, 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 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 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 Novel IoT Based Accident Detection and Rescue System
(IEEE, 2020-08) Karmokar, Pranto; Bairagi, Saikot; Mondal, Anuprova; Nur, Fernaz Narin; Moon, Nazmun Nessa; Karim, Asif; Yeo, Kheng Cher
In South-East Asian cities such as Delhi, Dhaka road accidents are a very common occurrence which brings disaster to human lives as well as infrastructures. Sometimes people cannot reach hospitals prompt after an accident because of the traffic jam, deficit of ambulance, lack of a mechanism to timely propagate information to the appropriate authority. To ensure the safety of lives, this paper proposes an automated IoT based effective accident detection system. Immediately after an incident, the data information is sent to the webserver, instant SMS is forwarded to the victim's acquaintances and also to the relevant authorities such as traffic control room, nearby police station, ambulance service. To evaluate the performance of the system, a simulated road scenario has been designed. The result obtained after a thorough integration and system testing demonstrates that the proposed system not only achieves the stated goal of the research but also can deliver the expected outcome in a rather cost-effective way.
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 Proposition for a Low-Cost Effective Attendance Management System
(IEEE, 2020-06) Alam, Mohammad Jahangir; Faisal, Fahad; Karim, Asif
In this paper, a holistic methodology has been followed to gauge the quality of service for real-time attendance system. The experiment was done to compare the performance of an attendance server which will be cost-effective but reliable. To reduce the cost, users are connected to the biometric attendance device with a local server where the data is updated instantly. As a result, the chance of data loss is almost none. The available devices in the market are very expensive whereas our complete system may cost around USD50. On the other hand, most of the devices produce multiple data for a single user on a specific day as the data fetched from the internal memory of the respective device. But our proposed system provides a very well-ordered data for easy tracking of attendance. Moreover, our proposed framework requires a marginal power to drive the whole system. The complete system can be implemented using regular hardware and software at a very low cost.
AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images
(IEEE, 2023-02-14) Shamrat, F M Javed Mehedi; Akter, Shamima; Azam, Sami; Karim, Asif; Ghosh, Pronab; Hasib, Khan Md.; Boer, Frisode; Ahmed, Kawsar
Alzheimer’s disease is largely the underlying cause of dementia due to its progressive neurodegenerative nature among the elderly. The disease can be divided into five stages: Subjective Memory Concern (SMC), Mild Cognitive Impairment (MCI), Early MCI (EMCI), Late MCI (LMCI), and Alzheimer’s Disease (AD). Alzheimer’s disease is conventionally diagnosed using an MRI scan of the brain. In this research, we propose a fine-tuned convolutional neural network (CNN) classifier called AlzheimerNet, which can identify all five stages of Alzheimer’s disease and the Normal Control (NC) class. The ADNI database’s MRI scan dataset is obtained for use in training and testing the proposed model. To prepare the raw data for analysis, we applied the CLAHE image enhancement method. Data augmentation was used to remedy the unbalanced nature of the dataset and the resultant dataset consisted of 60000 image data on the 6 classes. Initially, five existing models including VGG16, MobileNetV2, AlexNet, ResNet50 and InceptionV3 were trained and tested to achieve test accuracies of 78.84%, 86.85%, 78.87%, 80.98% and 96.31% respectively. Since InceptionV3 provides the highest accuracy, this model is later modified to design the AlzheimerNet using RMSprop optimizer and learning rate 0.00001 to achieve the highest test accuracy of 98.67%. The five pre-trained models and the proposed fine-tuned model were compared in terms of various performance matrices to demonstrate whether the AlzheimerNet model is in fact performing better in classifying and detecting the six classes. An ablation study shows the hyperparameters used in the experiment. The suggested model outperforms the traditional methods for classifying Alzheimer’s disease stages from brain MRI, as measured by a two-tailed Wilcoxon signed-rank test, with a significance of < 0.05.
An Automated System in ATM Booth Using Face Encoding and Emotion Recognition Process
(ACM International Conference Proceeding Series, 2020) Chowdhury, Atiqul Islam; Shahriar, Mohammad Munem; Islam, Ashraful; Ahmed, Eshtiak; Karim, Asif; Islam, Mohammad Rezwanul
Nowadays, the banking transaction system is more flexible than the previous one. When the banking sector introduces the ATM booth to us, it was a step ahead to ease the human effort. Here, ATM booth is an automated teller machine that gives out money to the consumer by inserting a card in it. All ATM booths support both credit and debit cards for the transaction, and this has saved everyone's time. But still, there are some certain situations, i.e., forgetting the card authentication details for a transaction can ruin a consumer's day. For this reason, this paper has tried to propose a system that will help everyone regarding this situation. This proposed system is about face encoding process with an emotion recognition test for making transactions faster and accurate, based on Convolutional Neural Network (CNN). However, normal card transactions can still be possible besides using the proposed system. FER2013 dataset was used for training and then tested the model using our own sample images. The result shows that the proposed system can correctly separate ‘Happy’ faces from other emotional faces and allow the transaction to proceed.
An Investigation into the Level of Valence Offered By Different Pointing Devices against Challenging Tasks
(International Conference on Advanced Computer Science and Information Systems (ICACSIS), 2020) Ahmed, Eshtiak; Islam, Ashraful; Ashraf, Mohsena; Khan, Md. Ibrahim; Chowdhury, Atiqul Islam; Karim, Asif
Pointing devices are the primary media of interac-tion between humans and computers. The three most popular pointing devices used in computers (both portable and non-portable) are mouse, touchpad and nubs (joystick). They have their different advantages and use cases while being targeted to different user groups. The aim of this study was to investigate whether the aforementioned pointing devices have different effects on human valence. A total of 12 participants were recruited for the experiment. Each participant completed a pointing reaction test with every pointing device aforementioned, where they selected as many randomly appearing circles as possible in a given amount of time. Then, subjective ratings of emotional valence and arousal were collected, and the effects of the pointing device used on these ratings were investigated. Our study shows that the valence rating of using the mouse were significantly higher in challenging scenarios, compared to the likes of touchpad and nub.
Analysis of Complex Networks for Security Issues Using Attack Graph
(2019 International Conference on Computer Communication and Informatics, IEEE, 2019-09-02) Musa, Tanvirali; Yeo, Kheng Cher; Azam, Sami; Shanmugam, Bharanidharan; Karim, Asif; Boer, Friso De; Nur, Fernaz Narin; Faisal, Fahad
Organizations perform security analysis for assessing network health and safe-guarding their growing networks through Vulnerability Assessments (AKA VA Scans). The output of VA scans is reports on individual hosts and its vulnerabilities, which, are of little use as the origin of the attack can't be located from these. Attack Graphs, generated without an in-depth analysis of the VA reports, are used to fill in these gaps, but only provide cursory information. This study presents an effective model of depicting the devices and the data flow that efficiently identifies the weakest nodes along with the concerned vulnerability's origin.The complexity of the attach graph using MulVal has been greatly reduced using the proposed approach of using the risk and CVSS base score as evaluation criteria. This makes it easier for the user to interpret the attack graphs and thus reduce the time taken needed to identify the attack paths and where the attack originates from.
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.
COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
(Daffodil International University, 2021-11-13) Akter, Shamima; Shamrat, F. M. Javed Mehedi; Chakraborty, Sovon; Karim, Asif; Azam, Sami
COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, theWilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.
Covid-19 Detection Using Deep Learning Algorithm on Chest X-ray Images
(Biology, 2021-11) Akter, Shamima; Shamrat, F M Javed Mehedi; Chakraborty, Sovon; Karim, Asif; Azam, Sami
COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, Dense Net, EfficientNetB7, Alex Net, and Google Net. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.
Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification
(Scopus, 2021) Tasnim, Zarrin; Chakraborty, Sovon; Shamrat, F. M. Javed Mehedi; Chowdhury, Ali Newaz; Nuha, Humaira Alam; Karim, Asif; Zahir, Sabrina Binte; Billah, Md. Masum
In recent years, the area of Medicine and Healthcare has made significant advances with the assistance of computational technology. During this time, new diagnostic techniques were developed. Cancer is the world's second-largest cause of mortality, claiming the lives of one out of every six individuals. The colon cancer variation is the most frequent and lethal of the numerous kinds of cancer. Identifying the illness at an early stage, on the other hand, substantially increases the odds of survival. A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost. In this research, CNN models are employed to analyse imaging data of colon cells. For colon cell image classification, CNN with max pooling and average pooling layers and MobileNetV2 models are utilized. To determine the learning rate, the models are trained and evaluated at various Epochs. It's found that the accuracy of the max pooling and average pooling layers is 97.49% and 95.48%, respectively. And MobileNetV2 outperforms the other two models with the most remarkable accuracy of 99.67% with a data loss rate of 1.24.
Deep Learning-Based Analysis of COVID-19 X-Ray Images: Incorporating Clinical Significance and Assessing Misinterpretation
(SAGE Publications, 2023-11-06) Bhuiyan, Md. Rahad Islam; Azam, Sami; Montaha, Sidratul; Jim, Risul Islam; Karim, Asif; Khan, Inam Ullah; Brady, Mark; Hasan, Md. Zahid; Boer, Friso De; Mukta, Md. Saddam Hossain
COVID-19, pneumonia, and tuberculosis have had a significant effect on recent global health. Since 2019, COVID-19 has been a major factor underlying the increase in respiratory-related terminal illness. Early-stage interpretation and identification of these diseases from X-ray images is essential to aid medical specialists in diagnosis. In this study, (COV-X-net19) a convolutional neural network model is developed and customized with a soft attention mechanism to classify lung diseases into four classes: normal, COVID-19, pneumonia, and tuberculosis using chest X-ray images. Image preprocessing is carried out by adjusting optimal parameters to preprocess the images before undertaking training of the classification models. Moreover, the proposed model is optimized by experimenting with different architectural structures and hyperparameters to further boost performance. The performance of the proposed model is compared with eight state-of-the-art transfer learning models for a comparative evaluation. Results suggest that the COV-X-net19 outperforms other models with a testing accuracy of 95.19%, precision of 96.49% and F1-score of 95.13%. Another novel approach of this study is to find out the probable reason behind image misclassification by analyzing the handcrafted imaging features with statistical evaluation. A statistical analysis known as analysis of variance test is performed, to identify at which point the model can identify a class accurately, and at which point the model cannot identify the class. The potential features responsible for the misclassification are also found. Moreover, Random Forest Feature importance technique and Minimum Redundancy Maximum Relevance technique are also explored. The methods and findings of this study can benefit in the clinical perspective in early detection and enable a better understanding of the cause of misclassification.
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%).