Enhancing the Deep Learning-Based Breast Tumor Classification Using Multiple Imaging Modalities: A Conceptual Model

Breast tumors’ preliminary and unambiguous prognosis is critical for early detection and diagnosis. A specific study has established automated techniques that use only science imaging modalities to speculate on breast tumor development. Several types of research, however, have suggested rephrasing the current literature's breast tumor classifications. This study reviewed various imaging modalities for breast tumors and discussed breast tumor segmentation and classification using preprocessing, machine learning, and deep neural network techniques. This research aims to classify malignant and benign breast tumors using appropriate medical image modalities and advanced neural network techniques. It is critical to improving strategic decision analysis on various fronts, including imaging modalities, datasets, preprocessing techniques, deep neural network techniques, and performance metrics for classification. They used preprocessing techniques such as augmentation, scaling, and image normalization in the respective investigation to minimize the irregularities associated with medical imaging modalities. In addition, we discussed various architectures for deep neural networks. A convolutional neural network is frequently used to classify breast tumors based on medical images to create an efficient classification paradigm. It could be an existing network or one that has been developed from scratch. The accuracy, area-under-the-curves, precision, and F-measures metrics of the developed classification paradigm will be used to evaluate its performance.