Now-a-days, breast cancer is the most crucial
problem amongst men and women. A massive number of people
are invaded with breast cancer all over the world. An early
diagnosis can help to save lives with proper treatment. Recently,
computer-aided diagnosis is becoming more popular in medical
science as well as in cancer cell identification. Deep learning
models achieve excessive attention because of their performance
in identifying cancer cells. Mammography is a significant
creation for detecting breast cancer. However, due to its complex
structure, it is challenging for doctors to identify. This study
provides a convolutional neural network (CNN) approach to
detecting cancer cells early. Dividing benign and malignant
mammography images can significantly improve detection and
accuracy levels. The BreakHis 400X dataset is collected from
Kaggle and DenseNet-201, NasNet-Large, Inception ResNet-V3,
Big Transfer (M-r101x1x1); these architectures show impressive
performance. Among them, M-r101x1x1 provides the highest
accuracy of 90%. The main priority for this research work is to
classify breast cancer with the highest accuracy with selected
neural networks. This study can improve the systematic way of
early-stage breast cancer detection and help physicians' decision-
making