Detection and Classification of Breast Cancer from Mammogram Images Using Adaptive Deep Learning Technique

Breast cancer is one of the deadliest diseases that affect women over the age of 40. The death rate from cancer is reduced when it is detected and diagnosed early. The removal of cancer cells from the infected region before they move to other organs via lymph nodes remains a serious challenge. The proposed technique aims to create a reliable method for detecting breast cancer cells in their early stages utilizing mammogram scans. A mammogram is a breast x-ray image. Mammograms are used as the first stage in the detection of breast cancer by medical professionals. To detect breast cancer indications from Mammogram images, the proposed method employs an Adaptive Deep Convolution Neural Network (ADCNN). The algorithm automatically identifies breast cancer as Normal, Benign, or Malignant based on the images. The study experiment is carried out to estimate the region of infection in which the cancer cells have converged using a convolution neural network method with a 99% accuracy.