Machine-driven techniques for early-stage tumor identification and categorization in Digital Mammography: A comprehensive overview
Year: 2025
Type: article
Abstract: Breast cancer remains a critical research focus in medical image analysis, being a leading cause of mortality among women. Digital mammography enhances early detection accuracy, crucial for improved prognosis. By 2020, breast cancer is projected to account for 25% of all cancer cases, characterized by uncontrolled cell proliferation in breast tissue. X-ray imaging can reveal tumor formation, with malignancy defined by metastatic potential. Traditional diagnostic approaches, often time-consuming and operator-dependent, necessitate more efficient detection methods. This study proposes an innovative deep learning-based classification system for automated breast cancer identification using biopsy images. The model's performance is evaluated using statistical metrics including precision, recall, and accuracy. By addressing key challenges in AI-assisted risk assessment, this research aims to accelerate the integration of advanced predictive tools, potentially optimizing and personalizing mammography screening programs in the future.
Authors: Ravindra Moje, Harshada Mhetre, Mangal Patil, Prashant Chougule, Pramod Jadhav, Priyanka Paygude, Shwetambari Chiwhane
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409303327