Cancer is the second highest cause of death as reported by the World Health Organization. A correct diagnosis of Breast Cancer ensures that appropriate treatment plans and procedures are provided to patients. Due to the disparity in the pathologists’ skills, manual histopathology examinations conducted by them are complex, time-intensive, and might be vulnerable to misinterpretations. Using Deep Learning algorithms, the key concept of our proposed system is to diagnose breast cancer types from microscopy biopsy images. The proposed system will detect whether the abnormal lesions presented in these images are benign or malignant and if it is malignant, it will be stated if it is an In-Situ Carcinoma or Invasive Carcinoma. We seek to reduce the time results taken to reach patients who are in both physical and emotional distress, as well as reducing the possibility of misdiagnosis that might lead to more severe complications in patients’ lives.

1.  Automate the histopathology complex process in diagnosing the breast cancer type.

2.  Cutting down the chances of misdiagnosis.

3.  Reduce the time the results take to reach the patients.

4.  Reduce the pathologists’ workload.

Our proposed system is designed to detect the type of breast cancer(Benign, In-Situ Carcinoma, Invasive Carcinoma). The system will detect breast cancer faster than normal pathology procedures. It aims to cut down the chances of misdiagnosis and reduce the time they spend until they receive the results to start the right treatment path.

The system will:

• Detect if the breast biopsy image is normal or abnormal.

• Detect the breast cancer type if it is abnormal.

• Generate a report with the patients’ data and results.

• Does not detect the breast cancer stage.