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

2024-07-03

AI Summary

Early breast cancer diagnosis faces clinical challenges due to indistinct tumor boundaries and inconsistent quality in ultrasound images, limiting the accuracy of segmentation and classification. To address this, we propose a dual-task end-to-end framework: (1) PMAD-LinkNet integrates a Precision Mapping Mechanism (PMM) with morphology-adaptive decoding to refine pixel-level tumor boundaries; (2) CSFEC-Net introduces a Component-Specific Feature Enhancement Module (CSFEM) and multi-level attention to enable differentiated focusing and decoupled discrimination among benign, malignant, and normal tissues—first reported for three-class tissue characterization. Evaluated on standard benchmarks, our method achieves 98.1% segmentation accuracy, 96.9% IoU, and 97.2% Dice score; classification accuracy reaches 99.2%, with F1-score, precision, and recall all ≥99.1%, significantly outperforming state-of-the-art models including U-Net and ResUNet.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Browser and end-point security

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.

Problem

• Enhancing breast cancer segmentation accuracy
• Improving diagnostic precision with deep learning
• Classifying benign, malignant, and normal tissues

Innovation

• Precision mapping mechanism
• Component-specific feature enhancement
• Attention-driven LinkNet segmentation