Breast Cancer Diagnosis through Mammographic Image Using MSVM Algorithm

D. Vijay Kumar Reddy and Ramireddygari Santan Kumar

International Journal of Scientific Research in Science and Technology, 2025, vol. 12, issue 3, 1151-1158

Abstract: Breast cancer screening is a critical area of medical diagnostics, where the accuracy and performance of radiologists play a pivotal role in early detection and diagnosis. In this study, we present a novel approach aimed at enhancing radiologists' performance in breast cancer screening through the optimization of parameters for a Multi-Class Support Vector Machine (MSVM). We compare the results of our proposed method against an existing approach based on Deep Neural Networks (DNN) in terms of accuracy, specificity, and the types of cancer detected, including both benign and malignant cases. The existing method employs DNN as the primary algorithm, achieving an accuracy rate of 92.8%. While this performance is commendable, our proposed method, leveraging the power of MSVM with optimized parameters, surpasses it with an accuracy rate of 93.5%. This improvement is of paramount significance in the context of breast cancer screening, where even small increments in accuracy can have substantial positive impacts on patient outcomes. Furthermore, when considering specificity, the existing DNN-based method achieves a specificity rate of 87.4%. In contrast, our proposed method utilizing MSVM parameters achieves a specificity rate of 88%. This enhancement in specificity is vital, as it minimizes false positives, reducing patient anxiety and unnecessary follow-up procedures. Notably, both methods excel in detecting both benign and malignant cases of breast cancer. Our proposed MSVM-based approach maintains the capability to identify both types of cancer, aligning with the existing DNN-based method in this regard. Finally the potential of utilizing Multi-Class Support Vector Machine (MSVM) parameters to enhance radiologists' performance in breast cancer screening. By achieving a higher accuracy rate and improved specificity, our proposed method empowers healthcare professionals with a more effective tool for early breast cancer detection. This research contributes to the ongoing efforts to improve breast cancer screening outcomes, ultimately benefiting patients and healthcare systems alike.

Keywords: Breast cancer; CNN Classifier; FCM; Mamograms; image processing (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v12:y2025:i3:id:933

DOI: 10.32628/IJSRST25123128

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