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A novel framework for Breast cancer scoring based on machine learning techniques using Immunohistochemistry images | ||
| Computational Methods for Differential Equations | ||
| مقاله 8، دوره 13، شماره 4، دی 2025، صفحه 1177-1188 اصل مقاله (915.9 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22034/cmde.2024.63113.2810 | ||
| نویسندگان | ||
| Hasanain Hayder Razzaq* 1؛ Rozaida Ghazali2؛ Loay E. George3؛ Muhammad Zulqarnain4 | ||
| 11. 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. | ||
| 2Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Batu Pahat, 86400, Johor, Malaysia. | ||
| 3University of Information Technology and Communication (UoITC), 10001, Baghdad, Iraq. | ||
| 4Department of Computer Science & IT, Cholistan University of Veterinary and Animal Sciences, Bahawalpur, Punjab, Pakistan. | ||
| چکیده | ||
| 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 introduce a hybrid approach for breast cancer diagnosis, which is 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. The number of the sample was 598, divided into training 70% and testing 30%. The 5-fold cross-validation was used to assess the proposed approach. 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. Additionally, this study evaluated the efficacy of the proposed model in comparison to various other traditional breast cancer detection approaches. | ||
| کلیدواژهها | ||
| Breast cancer؛ Immunohistochemistry؛ Segmentation؛ Enhancement؛ RGB؛ HSV | ||
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آمار تعداد مشاهده مقاله: 192 تعداد دریافت فایل اصل مقاله: 356 |
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