- Due to the highest mortality rate in the globe, cancer still poses a severe threat to individuals today. It is because there is insufficient early cancer detection. One of the most deadly diseases for women worldwide and the most frequently detected non-skin cancer in women is breast cancer carcinoma. The probability of a successful treatment is significantly increased by diagnostic testing of this lifethreatening disease. The traditional method of diagnosing cancer highly depends on the technician’s or doctor’s ability in spotting anomalies in the human body. Oncethe breast’s cell tissue starts to divide irregularly and aggressively, breast cancer develops. Bosom malignant tumor can be classified into five stages (0–IV), each of which reflects the severity of the disease within the patient’s body. Mammary gland illness is complicated and has many varieties of clinical effects, which makes it difficult to predict and treat. Additionally, the ability to more accurately predict the onset of a malignant infection will benefit breast cancer patients in planning their future course of treatment under their doctor’s directions. It is particularly challenging to predict breast cancer because of its great heterogeneity and complicated features. This projectexamines the accuracy of multiple CNN models and focuses on the prediction of Breast disease using mammography images.Therefore in the study, we explore various CNN models to identify malignancy in mammography scans, including ResNet- 50, VGG-16, Alex Net, and Google Net. Additionally, we are going to build a special Xception model with 70% accuracy to diagnose breast cancer and compare each model.

Keywords : Alex Net, Breast Cancer, Carcinoma, CNNConvolutional Neural Network, Deep Learning, Diagnose, Google Net, Mammogram, Malignant infection, Machine learning, ResNet-50, VGG-16.