• Mrs. Nandhini A
• Mythili K

Among women with cancer, breast cancer is the primary cause of death. Computeraided diagnosis is a useful tool that helps doctors make early diagnoses and increases patient chances of recovery. Because the medical industry is so sensitive, it is imperative that artificial intelligence (AI) be used there. This indicates that a major problem is the classification algorithms' low accuracy in cancer detection. This issue is particularly noticeable in cases of fuzzy mammography images. The traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) methodologies are presented in this research using convolutional neural networks (CNNs). The TCNN and SCNN techniques provide a contribution by resolving the scale and shift issues that cause mammography pictures to become fuzzy. Furthermore, the flipped rotation-based method.

Breast cancer, cancer detection, mammography, convolutional neural network (CNN), traditional convolutional neural network (TCNN), supported convolutional neural network (SCNN).

"BREAST CANCER PREDICTING USING DEEP LEARNING WITH ARTIFICAL INTELLIGENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 9, page no.e325-e330, September-2024, Available :http://www.jetir.org/papers/JETIR2409441.pdf

"BREAST CANCER PREDICTING USING DEEP LEARNING WITH ARTIFICAL INTELLIGENCE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 9, page no. ppe325-e330, September-2024, Available at : http://www.jetir.org/papers/JETIR2409441.pdf