Automated Diagnosis System for Early Breast Cancer Detection: Automated Diagnosis System for Early Breast Cancer Detection

Jamal Othman Ali; Alan Anwer Abdulla

doi:10.63841/iue326995

Authors

Jamal Othman Ali Department of Information Technology, Sulaimani Polytechnic University, Sulaimani, Kurdistan, IRAQ Author
Alan Anwer Abdulla Department of Information Technology, University of Sulaimani, Sulaimani, Kurdistan, IRAQ; Department of Information Technology, University College of Goizha, Sulaimani, Kurdistan, IRAQ Author https://orcid.org/0000-0003-4713-3794

DOI:

https://doi.org/10.63841/iue326995

Keywords:

Breast Cancer, BOVW, Tamura, Descriptor, SVM, KNN.

Abstract

Given that manual disease diagnosis and treatment determination are time-consuming, expensive, and demand skilled professionals, developments in computer technology and medical imaging processing within healthcare environments have improved diagnostic accuracy and early disease detection. Since they overcome the limitations associated with manual diagnostic approaches, automated or computer-assisted diagnostic systems can serve as viable alternatives. These systems are known as computer-aided diagnostic (CAD) systems and are commonly utilised for cancer diagnosis and other medical conditions. Digital image processing plays a vital role in handling and examining medical images for cancer identification and detection. This research introduces a CAD system designed for early breast cancer detection. Following the segmentation of the region of interest (ROI) from mammographic images through thresholding segmentation methods, texture and shape characteristics are extracted. For textural feature extraction, Tamura and Bag of Visual Words (BoVW) techniques are utilised. Additionally, shape-based statistical features, including compactness, sphericity, area, and elongation, are extracted. The images are subsequently categorized as normal or abnormal through k-nearest neighbour (kNN) and support vector machine (SVM) classification algorithms based on the extracted features. Comprehensive experiments conducted on the well-known Mammographic Image Analysis Society (MIAS) mammography image dataset were used to assess the proposed system's performance. The experimental results demonstrate that the proposed system achieves superior performance compared to other existing systems, with an accuracy rate of 99.6%.

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Author Biographies

Jamal Othman Ali, Department of Information Technology, Sulaimani Polytechnic University, Sulaimani, Kurdistan, IRAQ

Jamal Ali is a [MSc student] at the Department of /Information Technology/ College of Informatics, Sulaimani Polytechnic University. He got the B.Sc. degree in Sulaimani Polytechnique university, the M.Sc. degree in Sulaimani Polytechnique university and His research interests are in medical image processing.
Alan Anwer Abdulla, Department of Information Technology, University of Sulaimani, Sulaimani, Kurdistan, IRAQ; Department of Information Technology, University College of Goizha, Sulaimani, Kurdistan, IRAQ

Dr. Alan A. Abdulla is an Assistant Prof. at the Department of Information Technology Faculty/College of Commerce, University of Sulaimani. He got the B.Sc. degree in University of Sulaimani, the M.Sc. degree in Buckingham University and the Ph.D. degree in Buckingham University. His research interests are in Digital image processing.

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Automated Diagnosis System for Early Breast Cancer Detection