Browsing by Author "Basalamah, Anas"

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A Systematic Review on Federated Learning in Medical Image Analysis
(IEEE, 2023-03-21) Sohan, MD Fahimuzzman; Basalamah, Anas
Federated Learning (FL) obtained a lot of attention to the academic and industrial stakeholders from the beginning of its invention. The eye-catching feature of FL is handling data in a decentralized manner which creates a privacy preserving environment in Artificial Intelligence (AI) applications. As we know medical data includes marginal private information of patients which demands excessive data protection from disclosure to unexpected destinations. In this paper, we performed a Systematic Literature Review (SLR) of published research articles on FL based medical image analysis. Firstly, we have collected articles from different databases followed by PRISMA guidelines, then synthesized data from the selected articles, and finally we provided a comprehensive overview on the topic. In order to do that we extracted core information associated with the implementation of FL in medical imaging from the articles. In our findings we briefly presented characteristics of federated data and models, performance achieved by the models and exclusively results comparison with traditional ML models. In addition, we discussed the open issues and challenges of implementing FL and mentioned our recommendations for future direction of this particular research field. We believe this SLR has successfully summarized the state-of-the-art FL methods for medical image analysis using deep learning.
An Optimized CNN Model Architecture for Detecting Coronavirus (COVID-19) with X-Ray Images
(Springer, 2022-04-15) Basalamah, Anas; Rahman, Shadikur
This paper demonstrates empirical research on using convolutional neural networks (CNN) of deep learning techniques to classify X-rays of COVID-19 patients versus normal patients by feature extraction. Feature extraction is one of the most significant phases for classifying medical X-rays radiography that requires inclusive domain knowledge. In this study, CNN architectures such as VGG-16, VGG-19, RestNet50, RestNet18 are compared, and an optimized model for feature extraction in X-ray images from various domains involving several classes is proposed. An X-ray radiography classifier with TensorFlow GPU is created executing CNN architectures and our proposed optimized model for classifying COVID-19 (Negative or Positive). Then, 2,134 X-rays of normal patients and COVID-19 patients generated by an existing open-source online dataset were labeled to train the optimized models. Among those, the optimized model architecture classifier technique achieves higher accuracy (0.97) than four other models, specifically VGG-16, VGG-19, RestNet18, and RestNet50 (0.96, 0.72, 0.91, and 0.93, respectively). Therefore, this study will enable radiologists to more efficiently and effectively classify a patient’s coronavirus disease.
COVID-19 Detection Using Machine Learning:
(Daffodil International University, 2022-03-02) Sohan, Md. Fahimuzzman; Basalamah, Anas; Solaimana, Md.
Millions of people are infected by the coronavirus disease 2019 (COVID-19) around the world. Within three months of its first report, it rapidly spread worldwide with thousands of deaths. Since that time, not only underdeveloped and developing countries, but also developed countries have suffered from insufficient medical resources and diagnoses. In this circumstance, researchers from medical and engineering fields have tried to develop automatic COVID-19 detection toolkits using machine learning (ML) techniques. The dataset is the fundamental element of any detection tool; therefore, most of the ML-based COVID-19 detection research was conducted used chest x-ray and computed tomography (CT) image datasets. In our study, we collected a series of publicly available unique COVID-19 x-ray and CT image datasets, then assessed and compared their performances using our proposed 22 layer convolutional neural network model along with ResNet-18 and VGG16. We investigated eight individual datasets known as Twitter, SIRM x-ray, COVID-19 Image Repository, EURORAD, BMICV, SIRM CT, COVID-CT, and SARS-CoV-2 CT. Our model obtained classification accuracy of 91%, 81%, 59%, 98%, 58%, 79%, and 97%, respectively. Our proposed model obtained the highest classification accuracy using four datasets (Twitter, COVID-19 Image Repository, COVID-CT, and SARS-CoV-2 CT). Similarly, ResNet-18 only utilized three (EURORAD, BMICV, and SIRM CT), whereas VGG16 only utilized the SIRM x-ray dataset. Results of this investigation indicate a significant comparison chart among the performance of the datasets. Indeed, our study is a large-scale assessment of existing COVID-19 x-ray and CT image datasets. And to the best of our knowledge, this is the first performance comparison study that includes all publicly available COVID-19 datasets.