Browsing by Author "Boer, Friso De"

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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.
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 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.
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.
Ergonomic Risk Prediction for Awkward Postures From 3D Keypoints Using Deep Learning
(IEEE, 2023-10-20) Hossain, MD. Shakhaout; Azam, Sami; Karim, Asif; Montaha, Sidratul; Quadir, Ryana; Boer, Friso De; Altaf-Ul-Amin, MD.
Work-related musculoskeletal ailments are injuries or disorders of the joints, muscles, nerves, or tendons caused by repetitive tasks and jobs that require uncomfortable postures. REBA (Rapid Entire Body Assessment) is a widely used assessment method for examining occupational ergonomics in areas where musculoskeletal disorders (MSDs) are common. REBA assessment necessitates the presence of a professional evaluator who monitors workers’ motions and postures, which takes time and has limitations in terms of real-world implementation. With the progress of deep learning-based human posture estimate algorithms, postural risk assessment has become an important and complex research area. We present a technique for forecasting REBA risk levels using 3D coordinates of human body position as input data in this study. We calculated REBA risk scores for various body segments and overall risk rating for corresponding action level for each body position using 3D keypoints from the widely renowned Human 3.6M dataset, which is a significant contribution for future research work in this arena. Using this vast ground truth dataset, a unique DNN model was created to forecast the REBA risk level for measuring the full body’s postural risk. REBA Ground Truth dataset is highly imbalanced which coped with data augmentation for the rare classes. To determine the optimal model configuration based on highest accuracy, ablation study is conducted by tuning different hyper-parameters. The proposed model, post-ablation study, attained 89.07% accuracy score on a test set of 128,046 samples from Nadam optimizer with a learning rate of 0.001 and batch size of 512.
High-Precision Multiclass Classification of Lung Disease Through Customized MobileNetV2 From Chest X-Ray Images
(Elsevier, 2023-02-10) Shamrat, FM Javed Mehedi; Azam, Sami; Karim, Asif; Ahmed, Kawsar; Bui, Francis M.; Boer, Friso De
In this study, multiple lung diseases are diagnosed with the help of the Neural Network algorithm. Specifically, Emphysema, Infiltration, Mass, Pleural Thickening, Pneumonia, Pneumothorax, Atelectasis, Edema, Effusion, Hernia, Cardiomegaly, Pulmonary Fibrosis, Nodule, and Consolidation, are studied from the ChestX-ray14 dataset. A proposed fine-tuned MobileLungNetV2 model is employed for analysis. Initially, pre-processing is done on the X-ray images from the dataset using CLAHE to increase image contrast. Additionally, a Gaussian Filter, to denoise images, and data augmentation methods are used. The pre-processed images are fed into several transfer learning models; such as InceptionV3, AlexNet, DenseNet121, VGG19, and MobileNetV2. Among these models, MobileNetV2 performed with the highest accuracy of 91.6% in overall classifying lesions on Chest X-ray Images. This model is then fine-tuned to optimise the MobileLungNetV2 model. On the pre-processed data, the fine-tuned model, MobileLungNetV2, achieves an extraordinary classification accuracy of 96.97%. Using a confusion matrix for all the classes, it is determined that the model has an overall high precision, recall, and specificity scores of 96.71%, 96.83% and 99.78% respectively. The study employs the Grad-cam output to determine the heatmap of disease detection. The proposed model shows promising results in classifying multiple lesions on Chest X-ray images.
Smart Electrification of Rural Bangladesh through Smart Grids
(Scopus, 2021) Debnath, Dhrupad; Siddique, Abdul Hasib; Hasan, Mehedi; Faisal, Fahad; Karim, Asif; Azam, Sami; Boer, Friso De
A smart grid is a new technology that integrates power systems with communication systems. It is an intelligent and efficient management system that has self-healing capabilities. The smart grid can be applied to manage networks that integrate different types of renewable resources for power generation. Bangladesh is currently experiencing severe power deficiency. Renewable energy sources such as solar power and biogas can play an important role in this scenario, especially in rural areas where electricity is even scarcer. By applying prototype concepts of smart grid, power generation from renewable resources and efficient load management can be achieved by a centralized control center. This will control the on-off sequence of the load and maintain the system stability. In this paper, different aspects of implementing a prototype of the smart grid in the rural areas of Bangladesh are discussed.
Using Blockchain Technology for File Synchronization
(IOP Conference Series: Materials Science and Engineering, 2019) Khan, MD. Ibrahim; Faisal, Fahad; Azam, Sami; Karim, Asif; Shanmugam, Bharanidharan; Boer, Friso De
Modern storage technology has shifted from traditional offline state to cloud based technology since some time now. Because of this transition, the present society is now more dependent on the online storage solutions. Synchronization of files and keeping a history of changes are critical parts of any cloud system. Therefore, an implementation of Blockchain Technology with traditional file synchronization and versioning system can be extremely fruitful. Blockchain is not a new technology, but recently its importance has sky-rocketed as the society is moving towards the decentralized World Wide Web. Blockchain is “an open, distributed ledger that can record transactions between two parties efficiently and in a verifiable and permanent way” [1]. Blockchain provides immutable data storage and access with the combination of Proof-of-Work [2, 3]. Due to such appealing features, the study undertaken here investigates and proposes a Blockchain based resilient cloud storage solution that makes a sound utilization of various properties fundamental to any Blockchain based framework.