Affiliation:
1. Department of Computer Science University of Kerala Thiruvananthapuram India
2. Smith School of Business Queen's University Kingston Ontario Canada
Abstract
AbstractMachine learning provides powerful techniques for several applications, including automated disease diagnosis through medical image classification. Recently, many studies reported that deep learning approaches have demonstrated significant performance and accuracy improvements over shallow learning techniques. The deep learning approaches have been used in many problems related to disease diagnoses, such as thyroid diagnosis, diabetic retinopathy detection, foetal localization, and breast cancer detection. Many deep learning methods have been reported in the recent past that uses medical images from various sources, such as healthcare providers and open data initiatives, and reported significant improvement in terms of precision, recall, and accuracy. This paper proposes a framework incorporating deep convolutional neural networks and an enhanced feature extraction technique for classifying medical data. To show the real‐world usability of the proposed approach, it has been used for the classification of COVID‐19 images from computed tomography scans. The experimental results show that the proposed approach outperformed some of the chosen baselines and obtained an accuracy of 98.91%, comparable with already reported accuracies.
Subject
Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Control and Systems Engineering
Reference65 articles.
1. Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier
2. Efficient 3D Deep Learning Model for Medical Image Semantic Segmentation
3. COVID‐19 prediction and detection using deep learning;Alazab M.;International Journal of Computer Information Systems and Industrial Management Applications,2020
4. COVID MTNet: COVID‐19 detection with multi‐task deep learning approaches;Alom Z.;arXiv preprint,2020
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献