A Deep Learning with Metaheuristic Optimization-Driven Breast Cancer Segmentation and Classification Model using Mammogram Imaging

Abstract

Cancer is the second leading cause of death globally, with Breast Cancer (BC) accounting for 20% of the new diagnoses, making it a major cause of morbidity and mortality. Mammography is effective for BC detection, but lesion interpretation is challenging, prompting the development of Computer-Aided Diagnosis (CAD) systems to assist in lesion classification and detection. Machine Learning (ML) and Deep Learning (DL) models are widely used in disease diagnosis. Therefore, this study presents an Optimized Graph Convolutional Recurrent Neural Network based Segmentation for Breast Cancer Recognition and Classification (OGCRNN-SBCRC) technique. In the preparation phase, images and masks are annotated and then classified as benign or malignant. To achieve this, the Wiener Filter (WF)-based noise removal and log transform-based contrast enhancement are used for preprocessing. The OGCRNN-SBCRC technique utilizes the UNet++ method for segmentation and the RMSProp optimizer for parameter tuning. In addition, the OGCRNN-SBCRC technique employs the ConvNeXtTiny Convolution Neural Network (CNN) approach for feature extraction. For BC classification and detection, the Graph Convolutional Recurrent Neural Network (GCRNN) model is used. Finally, the Aquila Optimizer (AO) model is employed for the hyperparameter tuning of the GCRNN approach. The simulation analysis of the OGCRNN-SBCRC methodology, using the BC image dataset, demonstrated superior performance with an accuracy of 99.65%, surpassing existing models.