A Comprehensive Survey of Radiomics and Machine Learning in Medical Image Processing

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.14 · Issue 3

Published

October 2025

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Open Access

Licence

CC BY-NC-SA 4.0

Ashutosh Kumar Singh

Radiomics, defined as the high-throughput extraction of quantitative features from medical images, represents a paradigm shift in diagnostic and prognostic medicine by enabling the discovery of imaging biomarkers beyond human perception. This review synthesizes existing literature on the integration of advanced image processing techniques with machine learning (ML) and deep learning (DL) methods in radiomics. It outlines the standard workflow—including image acquisition, segmentation, feature extraction, feature selection, and model development—while examining both its potential and technical challenges. Applications across pulmonary disease analysis, oncology, and cardiac risk prediction highlight the state-of-the-art. Key issues such as feature reproducibility, model interpretability, data heterogeneity, and the need for robust validation are critically discussed. The paper concludes by identifying future directions, including standardized protocols, explainable AI, multimodal data fusion, and the ethical deployment of radiomics in clinical practice.

Radiomics Machine Learning Deep Learning Image Processing Feature Extraction Predictive Modeling Quantitative Imaging Medical Imaging Analytics.

Ashutosh Kumar Singh (2025). A Comprehensive Survey of Radiomics and Machine Learning in Medical Image Processing. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.14, Issue 3. ISSN: 2319-4863.

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