• Sundari David
• Prof. Sandeep Kumar

Breast cancer is one of the dreadful diseases that affect women globally. The occurrences of cancerous masses in the breast region are the main cause for women to develop a breast cancer. Early detection of breast mass will increase the survival rate of women. In the proposed work, a model is developed by using the Transfer Learning (TL) with ensemble method for classifying the breast cancer masses (BCM) as Benign or Malignant. The proposed system uses techniques such as VGG19, DenseNet201 which are CNN-based and Inception V3 for recognizing images. TL employs the formerly gained knowledge on the newest model and also solves the problem of building from scratch. The proposed system is applied on the MIAS (Mammography Image Analysis Society) dataset. The evaluation is done based on various performance metrics such as F1-score, accuracy, recall and precision.

Breast Cancer masses (BCM), Transfer Learning, Ensemble, VGG1, DenseNet201, Inception V3

"Breast Cancer Classification using Transfer Learning with Ensemble Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.b357-b362, July-2022, Available :http://www.jetir.org/papers/JETIR2207140.pdf

"Breast Cancer Classification using Transfer Learning with Ensemble Method", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppb357-b362, July-2022, Available at : http://www.jetir.org/papers/JETIR2207140.pdf