Breast cancer image classification by using HCNN and LeNet5

Medical data from many sectors has greatly increased during the last 10 years. One of the main causes of death and illness among women worldwide is breast cancer. Breast cancer (BC) is one of the most common cancers in women; the death rate is high and holds the second position next to lung cancer. Breast cancer develops when cells in the mammary glands and the ducts that transfer milk to the nipple grow out of control. This is the first step in the progression of the disease. However, the time complexity of the available techniques is immense due to the many processes. Additionally, the current research attempts to improve the computation time involved in the detection process. Therefore, an effective hybrid deep learning model is introduced to improve the prediction performance and reduce the time consumption compared to the machine learning model. The breast cancer dataset, obtained from Kaggle, is used as the input data. A Wiener filter preprocessing technique is applied to enhance the image quality, with an active Wiener filter employed for this purpose. The segmentation step is achieved using a Modified Watershed Algorithm, which isolates the region of interest within the images. Finally, classification is performed using a hybrid deep learning model. This model combines a Convolutional Neural Network (CNN) with an Enhanced Recurrent Neural Network (ERNN), leveraging the strengths of both architectures. According to experimental results, the proposed Hybrid Convolutional Neural Network (HCNN) model achieves an accuracy of 96.12%, a precision of 96.99%, a recall of 97.52%, and an F-measure of 97.25%, outperforming other existing models.