Machine Learning in MRI-Based Cancer Characterization: Enhancing Precision and Early Detection
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This study examines how MRI and ML can improve cancer detection and early detection. Better cancer diagnostics and earlier detection are the goals. Test and evaluate machine learning methods for different cancers. These include CNNs, SVMs, RFs, and ensemble approaches. Public datasets were analyzed. The AUC-ROC, sensitivity, specificity, and detection rate were used to evaluate the results. Using radiomics features with deep learning architectures to improve diagnostic skills is also considered. Ensemble systems that use many machine learning (ML) methods outperform solo algorithms with an average precision of 92.7 percent. A list of the primary issues that must be addressed for practical translation is also provided. This requires larger and more diverse datasets and improved understanding and application of learned information. Multimodal integration and federated learning approaches may be studied in the future to solve current issues.
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