A framework for breast cancer prediction and classification using deep learning
Praveen Kumar Shukla, Aditya Ranjan Behera · International Journal of Computational Vision and Robotics · 2024
Breast cancer is a very common disease nowadays. But it is very important to identify and diagnose it at an early stage. So before identifying, it requires identifying and classifying the cancerous cell. Generally, in detecting cancerous cells, the 'mammography' process is more intuitive than any other methods. This is a method of computer aided diagnostic that includes digital image processing for detection of breast cancer. This article presents the method of detection of cancer affected cells and classifies normal patients to cancerous patients. Pre-processing operations are performed on mammographic images after normalisation of the mammographic images. To complete the task of prediction of cancer affected cells, a breast cancer prediction model architecture has been proposed with an accuracy of 94.87%. For the classification of cancerous patients and normal patients, VGG Net 19 architecture has been adopted with an accuracy of 97.27%. In the purposed framework model can be implemented practically as an application in the field of breast cancer diagnosis for a better result in a shorter period.