Breast cancer detection and classification using optimisation enabled deep learning model

This work aspires to discover a scheme for breast cancer disease detection and presents the integration of deep learning methods to minimise breast cancer risk in women's lives. The initial phase involves pre-processing the images through contrast enhancement and resizing. Subsequently, the pre-processed outputs undergo data augmentation, which has become a significant focus in recent deep learning domain. This step is essential for enhancing the diversity of the original dataset. Subsequently, for segmenting cancer region in the augmented images, the multi-input shape convolution neural network (MIScnn) scheme was employed. Finally, the breast cancer detection process is performed using the hybrid LeNet-ZFNet, which is the combination of LeNet and ZFNet scheme, to classify as either normal or malignant. Finally, experimental analysis revealed that proposed approach achieved maximal accuracy of 95.8%, sensitivity of 97.3%, and specificity of 93.3%.