Document Type : Research Paper
Authors
1
1. Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Batu Pahat, 86400, Johor, Malaysia. 2. College of Medicine, Jabir ibn Hayyan Medical University, Najaf, Iraq.
2
Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Batu Pahat, 86400, Johor, Malaysia.
3
University of Information Technology and Communication (UoITC), 10001, Baghdad, Iraq.
4
Department of Computer Science & IT, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Punjab, Pakistan.
Abstract
Breast cancer is classified as a serious disease in the medical field and there is no
doubt that breast cancer detection requires effective and accurate techniques. Integrating
deep learning (DL) and machine learning (ML) methods has shown promising results in
this area. In this research, we introduced a hybrid approach for breast cancer diagnosis
which centered on the analysis of immunohistochemical images. The proposed method
encompasses algorithms for image pre-processing, segmentation, extracting informative
indicators (such as relative cell area and intensity), and an algorithm for categorizing the
molecular harmonic subtype of breast cancer. Experimental results showcased the
effectiveness of the proposed hybrid method in achieving superior performance in the
detection of breast cancer, especially within breast cancer scoring systems. The
accuracy of our proposed approach, which involved combining HSV integration with
adaptive high boost filtering, reaches a peak at 96.5% when using SVC (linear kernel).
Moreover, the precision, recall, F1-score, and specificity metrics are recorded at
95.29%, 99.99%, 95.59%, and 99.28%, respectively.
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