A Comprehensive Review on Biomedical Image Classification using Deep Learning Models
Received: 13 August 2024 | Revised: 15 October 2024 | Accepted: 29 November 2024 | Online: 15 December 2024
Corresponding author: Mohamed Tounsi
Abstract
Medical imaging is one of the most efficient tools for visualizing the interior organs of the body and its associated diseases. Medical imaging is used to diagnose diseases and offer treatment. Since the manual examination of a massive number of Medical Images (MI) is a laborious and erroneous task, automated MI analysis approaches have been developed for computer-aided diagnostic solutions to reduce time and enhance diagnostic quality. Deep Learning (DL) models have exhibited excellent performance in the MI segmentation, classification, and detection process. This article presents a comprehensive review of the recently developed DL-based MIK classification models for various diseases. The current review aims to assist researchers and physicians of biomedical imaging in understanding the basic concepts and recent DL models. It explores recent MI classification techniques developed for various diseases. A thorough discussion on Computer Vision (CV) and DL models is also carried out.
Keywords:
medical image analysis, computer-aided diagnosis, imaging modalities, deep learning, computer visionDownloads
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Copyright (c) 2024 Mohamed Tounsi, Erahid Aram, Ahmad Taher Azar, Ahmed Al-Khayyat, Ibraheem Kasim Ibraheem
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