Exploiting Precision Mapping and Component-Specific Feature Enhancement for Breast Cancer Segmentation and Identification

TL;DR

This paper introduces novel deep learning frameworks that enhance breast cancer detection in ultrasound images through precise boundary segmentation and component-specific feature enhancement, achieving high accuracy and robustness.

Contribution

The paper presents a new precision mapping mechanism and a component-specific feature enhancement module for improved segmentation and classification of breast tumors in ultrasound images.

Findings

Segmentation accuracy of 98.1% and IoU of 96.9%.

Classification accuracy of 99.2% with high F1-score.

Outperforms existing CNN architectures in accuracy and robustness.

Abstract

Breast cancer is one of the leading causes of death globally, and thus there is an urgent need for early and accurate diagnostic techniques. Although ultrasound imaging is a widely used technique for breast cancer screening, it faces challenges such as poor boundary delineation caused by variations in tumor morphology and reduced diagnostic accuracy due to inconsistent image quality. To address these challenges, we propose novel Deep Learning (DL) frameworks for breast lesion segmentation and classification. We introduce a precision mapping mechanism (PMM) for a precision mapping and attention-driven LinkNet (PMAD-LinkNet) segmentation framework that dynamically adapts spatial mappings through morphological variation analysis, enabling precise pixel-level refinement of tumor boundaries. Subsequently, we introduce a component-specific feature enhancement module (CSFEM) for a component-specific feature-enhanced classifier (CSFEC-Net). Through a multi-level attention approach, the CSFEM magnifies distinguishing features of benign, malignant, and normal tissues. The proposed frameworks are evaluated against existing literature and a diverse set of state-of-the-art Convolutional Neural Network (CNN) architectures. The obtained results show that our segmentation model achieves an accuracy of 98.1%, an IoU of 96.9%, and a Dice Coefficient of 97.2%. For the classification model, an accuracy of 99.2% is achieved with F1-score, precision, and recall values of 99.1%, 99.3%, and 99.1%, respectively.