BCDCNN: breast cancer deep convolutional neural network for breast cancer detection using MRI images.

Breast cancer (BC) is a kind of cancer that is created from the cells in breast tissue. This is a primary cancer that occurs in women. Earlier identification of BC is significant in the treatment process. To lessen unwanted biopsies, Magnetic Resonance Imaging (MRI) is utilized for diagnosing BC nowadays. MRI is the most recommended examination to detect and monitor BC and explain lesion areas as it has a better ability for soft tissue imaging. Even though, it is a time-consuming procedure and requires skilled radiologists. Here, Breast Cancer Deep Convolutional Neural Network (BCDCNN) is presented for Breast Cancer Detection (BCD) using MRI images. At first, the input image is taken from the database and subjected to a pre-processing segment. Adaptive Kalman filter (AKF) is utilized to execute the pre-processing phase. Thereafter, cancer area segmentation is conducted on filtered images by Pyramid Scene Parsing Network (PSPNet). To improve segmentation accuracy and adapt to complex tumor boundaries, PSPNet is optimized using the Jellyfish Search Optimizer (JSO). It is a recent nature-inspired metaheuristic that converges to an optimal solution in fewer iterations compared to conventional methods. Then, image augmentation is performed that includes augmentation techniques namely rotation, random erasing and slipping. Afterwards, feature extraction is done and finally, BCD is conducted employing BCDCNN, wherein the loss function is newly designed based on an adaptive error similarity. It improves the overall performance by dynamically emphasizing samples with ambiguous predictions, enabling the model to focus more on diagnostically challenging cases and enhancing its discriminative capability. Furthermore, BCDCNN acquired 90.2% of accuracy, 90.6% of sensitivity and 90.9% of specificity. The proposed method not only demonstrates strong classification performance but also holds promising potential for real-world clinical application in early and accurate breast cancer diagnosis.