AUTHOR=Arooj Sahar , Atta-ur-Rahman  , Zubair Muhammad , Khan Muhammad Farhan , Alissa Khalid , Khan Muhammad Adnan , Mosavi Amir 

TITLE=Breast Cancer Detection and Classification Empowered With Transfer Learning

JOURNAL=Frontiers in Public Health

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2022.924432

DOI=10.3389/fpubh.2022.924432

ISSN=2296-2565

ABSTRACT=Breast cancer is a cancer that begins in the breast and spreads to other parts of the body. One of the most common types of cancer that kills women is breast cancer. When cells get uncontrollably large, cancer develops. There are various types of breast cancer. Proposed model discussed about Benign and Malignant breast cancer. In computer- aided diagnosis systems, Breast cancer identification and classification using histopathology and ultrasound images is a critical step. Investigators have demonstrated the ability to automate the tumor initial level identification and classification throughout the last few decades. However, research is hampered by the lack of dataset. Breast cancer can be detected early, allowing patients to obtain proper therapy and so increase their chances of survival. Deep learning (DL), Machine learning (ML) and Transfer learning (TL) techniques are used to solve many medical issues. Proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. The proposed article applied the transfer learning technique on three datasets, A, B, C and A2, A2 is the dataset A with 2 classes. In this article ultrasound images and histopathology images are used. The used model in this work is CNN-Alex net and trained this model according to the requirements of datasets. Results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than existing models on Dataset A, B, C and A2.