Affiliation:
1. Department of Computer Science HITEC University Taxila Pakistan
2. Department of Computer Engineering HITEC University Taxila Pakistan
3. Department of Information Systems College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia
4. School of Computing Engineering and Physical Sciences University of West of Scotland Scotland UK
5. Department of Physics, Physical Sciences Building Aberystwyth University Aberystwyth UK
Abstract
AbstractDiseases of the Gastrointestinal (GI) tract significantly affect the quality of human life and have a high fatality rate. Accurate diagnosis of GI diseases plays a pivotal role in healthcare systems. However, processing large amounts of medical image data can be challenging for radiologists and other medical professionals, increasing the risk of inaccurate medical assessments. Computer‐aided Diagnosis systems provide help to doctors for rapid and accurate diagnosis, thus resulting in saving lives. Recently, many techniques are found in the literature that uses deep Convolutional Neural Network (CNN) models for accurate disease classification. However, they have limitations in their ability to detect deformation‐invariant features and lack robustness. The diseased region is highlighted, using attention‐based image generation and superimposition with original images. A lightweight deep CNN model is employed to get significant features. These features are further reduced using a Cosine similarity‐based technique. The proposed framework is assessed using the Kvasir dataset. To verify the effectiveness of the proposed framework, vast experiments are conducted. The overall accuracy of 97.68%, 99.02% precision, 96.37% recall, and an F‐measure of 97.68% are achieved using the 810 significant features. This reduction in features resulted in a significant reduction in classification time. The robustness of the framework can be observed not only in terms of considerable improvement in accuracy, but also in terms of precision as well as recall, and F‐measure.
Publisher
Institution of Engineering and Technology (IET)
Subject
Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Information Systems
Cited by
2 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