CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis

Adegun, Adekanmi and Ogundokun, Roseline Oluwaseun and Adebiyi, Marion and Asani, E. Oluwatobi (2020) CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis. Handbook of Research on the Role of Human Factors in IT Project Management. pp. 164-172. ISSN 2331-768X

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Abstract

Machine learning technique such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyses and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in diagnosis and monitoring of some diseases. Diseases such as skin cancer, age�related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Depositing User:	Mr Uchechukwu F. Ekpendu
Date Deposited:	02 Jul 2021 10:53
Last Modified:	02 Jul 2021 10:53
URI:	https://eprints.lmu.edu.ng/id/eprint/3165

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