Anatomy of Breast Cancer Detection and Diagnosis Using a Support Vector Machine (SVM) and a Convolutional Neural Network (CNN)


  • Ishu Goel, Ravindra Kumar Vishwakarma


Breast Cancer, Diagnosis, Support Vector Machine (SVM), Convolutional Neural Network (CNN)


It is basic to distinguish breast cancer when achievable. This composition presents a clever way to deal with breast cancer grouping that utilizes profound learning and a couple of division draws near. A clever PC helped discovery (computer aided design) strategy is recommended to separate among harmless and dangerous mass cancers in pictures got from breast mammography. Two division strategies are applied in this computer aided design framework. The area of interest (return on initial capital investment) still up in the air in the main methodology, though the limit and district-based system is utilized in the subsequent methodology. Include extraction is finished utilizing the profound convolutional neural network (DCNN). Through the joining of two powerful machine learning draws near, Support Vector Machine (SVM) and Convolutional Neural Network (CNN), this study investigates the intricacies of breast cancer identification and diagnosis. By looking at the perplexing elements and examples in mammogram pictures, the review dives into the physical nuances that are fundamental for exact location and diagnosis. The review plans to work on the adequacy and accuracy of breast cancer location by using CNN and SVM calculations. This will prompt a significant improvement in tolerant results and early diagnosis. This study gives quick data about how to make more strong demonstrative instruments to battle breast cancer by completely looking at the connections between these calculations and the multifaceted physical designs found in breast imaging.


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How to Cite

Ishu Goel. (2024). Anatomy of Breast Cancer Detection and Diagnosis Using a Support Vector Machine (SVM) and a Convolutional Neural Network (CNN). International Journal of Intelligent Systems and Applications in Engineering, 12(22s), 92–98. Retrieved from



Research Article