Deep learning based computerized diagnosis of breast cancer using digital mammograms
Laxman Singh, R.L. Kashyap, Sovers Singh Bisht, Nagesh Sharma, Surya Prakash Sharma · 2024
Breast cancer (BC) is the second most common and deadly type of cancer in women, after skin cancer. The likelihood of a patient surviving breast cancer is greatly increased by early detection and classification. The authors of this work aim to create a deep learning (DL) model that can identify and categorize tumours in mammography images. When it comes to finding breast cancer as soon as possible before it becomes incurable, mammography is regarded as the gold standard. In the suggested work, we classified malignant and benign cells using pre-trained CNN architecture in conjunction with VGGNet-16, VGGNet-19, and Efficient Net BO. The results show how useful deep learning-based models can be in aiding radiologists in the interpretation of digital mammograms and validate the significance of these models for automated breast cancer diagnosis.