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

Adekanmi Adeyinka Adegun, Roseline Oluwaseun Ogundokun, Marion Olubunmi Adebiyi, Emmanuel Oluwatobi Asani

Source Title: Handbook of Research on the Role of Human Factors in IT Project Management

ISBN13: 9781799812791|ISBN10: 1799812790|EISBN13: 9781799812807

DOI: 10.4018/978-1-7998-1279-1.ch011

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MLA

Adegun, Adekanmi Adeyinka, et al. "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, edited by Sanjay Misra and Adewole Adewumi, IGI Global, 2020, pp. 164-172. https://doi.org/10.4018/978-1-7998-1279-1.ch011

APA

Adegun, A. A., Ogundokun, R. O., Adebiyi, M. O., & Asani, E. O. (2020). CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis. In S. Misra & A. Adewumi (Eds.), Handbook of Research on the Role of Human Factors in IT Project Management (pp. 164-172). IGI Global. https://doi.org/10.4018/978-1-7998-1279-1.ch011

Chicago

Adegun, Adekanmi Adeyinka, et al. "CAD-Based Machine Learning Project for Reducing Human-Factor-Related Errors in Medical Image Analysis." In Handbook of Research on the Role of Human Factors in IT Project Management, edited by Sanjay Misra and Adewole Adewumi, 164-172. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1279-1.ch011

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Abstract

Machine learning techniques 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 analyzes and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in the 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.

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

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