Exploring the Fusion of CNNs and Textural Features in Mammogram Interpretation
Bianca Iacob, Laura Silvia Dioşan · Procedia Computer Science · 2024
Breast cancer remains a critical global health concern, given the crucial role of early detection in achieving successful treatment outcomes. By harnessing the power of deep learning, our proposed methodology aims to discern intricate patterns and nuances in breast tissue textures, enabling robust discrimination between benign and malignant tumors. We start to search for solutions using two directions: an intelligent system that uses Convolutional Neural Networks (CNNs) over the images and another model that uses CNNs over textural features extracted from mammograms. This research comes as an extension to our previous work on the Classification of mammograms into benign and malignant types using textural features and shallow classifiers. While these methods provided valuable insights, we sought to explore the future of CNNs in increasing the accuracy of breast cancer detection.