Abstract: Breast cancer is one of the leading causes of women fatalities. Breast cancer is a worldwide disease with symptoms and features being similar in almost all regions. In India, in the past 25-30 years there has been a steep increase in cases of breast cancer being reported in a comparatively younger age group (40+). Indian women have a higher rate of fatality due to breast cancer compared to the global average. There is severe lack of awareness among rural as well as urban women resulting in the delay in diagnosis of cancer makes it difficult to fight. There are many instances when specialists disagree on opinions related to diagnosis hence, forcing patients to look for another opinion from a different specialist hereby wasting crucial time and monetary resources of a patient. Hence, with the aim to support a doctor’s diagnosis with an accurate, precise and quick diagnosis this Computer Aided Diagnosis (CADx) system has been developed. This model takes H and E stained images of breast tissues as input and classifies them into four classes namely Benign, In-Situ, Invasive and Normal. This program uses image processing techniques and Deep learning which is a class of Machine learning and AI. A Convolutional Neural Network has been used as the primary brain of this model since it detects distinctive features from the data without any human supervision or predefined set of features. This model is implemented using python and some useful deep learning libraries like TensorFlow and Keras. This model yields a 95.6 percent sensitivity for cancer. This model can help doctors and lab technicians to provide quick diagnosis and reduce conflict in opinions of specialists. A patient can get diagnosed in a more efficient and cost effective manner.