Early Breast Cancer Detection Among Patients Using a Deep Learning Image Processing Model

Breast cancer may be detected using ultrasonic pictures and a neural network technique based on deep convolutional networks. It is the goal of this article to compare breast cancer diagnosis using model networks of DL. Preprocessing images, classifying them, and assessing their performance are all part of the process. In this research, the proposed methodology has used the stacked VGG-16 deep learning model to detect breast cancer. The suggested algorithm is expected to produce accurate findings, which would eliminate human error in the diagnostic process and lower the cost of a cancer diagnosis. It compares the results of the VGG-16 architecture-based suggested system for automatically detecting breast cancer with those of the DL algorithm. Around 275,000 50-50-pixel RGB photo patches were used in this process. The simulation has done on the H&E dataset using Python as a simulation technology. Our proposed deep learning model has achieved a remarkable output concerning the evaluation metrics.