Subject Review: Diagnoses cancer diseases systems for most body's sections using image processing techniques
Mustansiriyah University, College of Education, Department of Computer Science, Baghdad, Iraq.
Review Article
Global Journal of Engineering and Technology Advances, 2021, 06(03), 056-062.
Article DOI: 10.30574/gjeta.2021.6.3.0031
Publication history:
Received on 22 January 2021; revised on 26 February 2021; accepted on 01 March 2021
Abstract:
Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.
Keywords:
Feed-Forward Back Propagation NN; A gray-level co-occurrence matrix (GLCM); Dense Scale Invariant Feature Transform (DSIFT); Deep convolutional neural network (DCNN).
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Copyright © 2021 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0