Bosom malignant growth is a deadly infection that is liable for the passing of ladies everywhere. Bosom Malignant growth
is recognized as the main worry for ladies deaths. There are different sorts of bosom disease. The proposed model talked
about harmless and dangerous bosom disease. In PC supported analysis frameworks, the distinguishing proof and
arrangement of bosom malignant growth utilizing histopathology and ultrasound pictures are basic advances. Deep learning
(DL), machine learning (ML), and transfer learning (TL) strategies are utilized to address numerous clinical issues. The
proposed approach is made to assist with the programmed recognizable proof and determination of bosom malignant
growth. Our principal commitment is that the proposed model utilized the transfer learning strategy. The model utilized
in this work is a tweaked CNN-AlexNet, which was prepared by the prerequisites of the datasets. This is likewise one of
the commitments of this work.
Key words: breast cancer, deep learning (DL), learning rate (LR),machine learning (ML), transfer learning (TL), convolutional neural network (CNN).
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