• Dr Deepa B.G
• Sirisha S
• Prateeksha S.G

Breast Cance (BC) is one of the deadly diseases and most dangerous disease which affects the lives of millions of women all over the world. Eventually over time, the number of breast cancer cases had been rapidly increased. Therefore, preventing Breast Cancer is a difficult task but still survival rate would be improvised if the disease is treated in early stages. Lot of improvements in breast cancer can be seen using deep learning techniques. The most common type among breast cancer is invasive ductal carcinoma. Histopathology images are used to detect breast cancer. from overall images 78,786 are observed as idc positive images, and 148,738 images are observed as IDC negative images. Deep learning method uses several methods like keras, maxpooling, Dropout, fatten etc. And also, our model EfficientNet uses a special method called compound scaling. our research work is also compared with different models. Therefore, after evaluation EfficientNet model provided results around 97.2% of accuracy is observed.

Breast Cancer, Invasive Ductal Carcinoma, Computer Assisted Diagnostics, Specificity, Sensitivity, recall, F1-Score, Deep learning, Breast Histopathology Images, Microwave Imaging, K-nearest neighbor.

"Revolutioinizing Breast Cancer Prediction Through Efficientnet-Based Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.h385-h390, June-2024, Available :http://www.jetir.org/papers/JETIR2406744.pdf

"Revolutioinizing Breast Cancer Prediction Through Efficientnet-Based Deep Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pph385-h390, June-2024, Available at : http://www.jetir.org/papers/JETIR2406744.pdf