Advanced Multi-View Convolutional-Recurrent Network for Breast Cancer Classification and Detection
A. AarthiResearch Scholar, Department of Electronics and Communication Engineering, Ponnaiyah Ramajayam Institute of Science and Technology (PRIST), Deemed to be University, Thanjavur, Tamil Nadu, India; Assistant Professor, Department of Electronics and Communication Engineering, Paavai Engineering College, Namakkal, Tamil Nadu, India. aarthiannadurai23@gmail.com0009-0004-6048-4083
Dr. Smitha Elsa PeterProfessor, Department of Electronics and Communication Engineering, Ponnaiyah Ramajayam Institute of Science and Technology (PRIST), Deemed to be University, Thanjavur, Tamil Nadu,India. smithasishaj@gmail.com0000-0003-2083-9113
Keywords: Detecting Breast Cancer, Multi-View Imaging, Convolutional Neural Networks (CNNS), Recurrent Neural Networks (RNNS), Gated Recurrent Units (Grus), Deep Learning, Medical Image Analysis, Hybrid Models, and Computer-Aided Diagnosis.
Abstract
Breast cancer is one of the most common and deadliest diseases that women globally experience. An early and accurate diagnosis may improve the chance of treatment success and increased survival rate. Current traditional diagnostic techniques used for breast cancer like mammography, ultrasound, and MRI are largely annotated by experts, creates variability and inconsistency in the diagnosis. New developments in artificial intelligence (AI) and deep learning could offer promising solutions for touchless high-precision breast cancer diagnosis. This study proposes an Advanced Multi-View Convolutional-Recurrent Network (AMVCRN), a unique design that integrates Convolutional Neural Networks used for extracting spatial features, with Recurrent Neural Networks, using Gated Recurrent Units – GRUs, for temporal sequence modeling. The hybrid model allows greater analysis of tumor features from multi-view images collected from different modalities and angles. The intended outcomes are to improve classification accuracy and reduce errors in persecution. Ultimately, it is intended to yield and sound a decision support system for clinical radiologists.