SURAKSHA: Segmenting Ultrasound Images using Real-Time Attention and Knowledge-based Structured Hybrid Architecture for Possible Breast Cancer Diagnosis

Abstract

Introduction: Breast cancer is one of the most common causes of death among women worldwide. Accurate and efficient diagnosis of breast cancer requires effective classification and segmentation of medical images. Though quite powerful, traditional deep learning mechanisms like Convolution Neural Networks (CNNs), Residual Neural Networks (ResNets), etc. are often computationally expensive, and resource-demanding. This calls for a Deep Learning approach to Constrained Quantization in Radiotherapy for Breast Cancer treatment by inheriting attention mechanisms.

Objectives: The primary objective of this research is to develop an efficient deep learning model for breast cancer diagnosis that enhances segmentation of medical images.

Methods: A novel deep learning architecture, SURAKSHA (Segmenting Ultrasound images using Real-time Attention and Knowledge-based Structured Hybrid Architecture) is proposed for possible Breast Cancer Diagnosis and identification of malignant tissues through the prediction of masks.

Results: The proposed model outperforms the state-of-the-art models by achieving accuracy (validation) of 93.01% on a lightweight cancer dataset, that is between 0.139% to 11.083% improvements from the existing models. Further, the possible variation in performance with changes in the dimensions of the select patch is studied.

Conclusions: The study presents an efficient deep learning-based segmentation to accurately detect malignant breast tissues, aiming to improve the effectiveness of radiotherapy.