Citation

Lew, Kai Liang and Toa, Chean Khim and Zhou, Pengfei and Lee, Chia Shyan and Kurniawan, Tetuko and Babale, Suleiman Aliyu and Zheng, Cheng (2025) AI-Assisted Analysis for Breast Cancer Imaging and Diagnostics. International Journal on Robotics, Automation and Sciences, 7 (1). pp. 111-119. ISSN 2682-860X

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Abstract

Breast cancer cases have increased by 0.5% each year. X-ray, CT-Scan, and magnetic resonance imaging have been used to detect cancer without harming the patient. However, these methods usually used manual screening to process medical images, which leads to longer processing time and increases the burden on the expert. With the help of deep learning, automation-driven breast cancer detection, segmentation, and explanation can be performed in the process, which can greatly reduce the processing time and the burden on experts. This paper proposed a deep learning model, S-YOLOv11 by combining YOLOv11 with SimAM attention mechanism and a GUI with integration of a large language model. The model is trained with 624 images and tested with 156 images. Several YOLO architectures were compared, including YOLOv8, YOLOv9, YOLOv10, and YOLOv11. The proposed model has outperformed the other models. In the detection task, 0.806 precision, 0.635 recall, and 0.724 mAP were achieved. In the segmentation task, 0.833 precision, 0.65 recall, and 0.739 mAP were achieved. In addition, the study also improved the functionality of the GUI by accessing the ChatGPT API. It is possible to generate medical analysis for breast cancer tumors, with the use of GUI for visualization.However, current research is still in the development stageand it needs to be put into clinical trials before it can be used.

Item Type:	Article
Uncontrolled Keywords:	Breast cancer
Subjects:	R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	26 Jun 2025 01:31
Last Modified:	26 Jun 2025 01:31
URII:	http://shdl.mmu.edu.my/id/eprint/14072

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