Machine Learning in Oncology: SVM-Based Classification of Lung, Breast and Liver Cancer from MRI Scans

In the continuous quest to enhance the precision of cancer diagnosis using MRI scans, this research harnessed the capabilities of the Support Vector Machine (SVM) algorithm for classifying distinct cancer types. Our methodological approach achieved a remarkable overall accuracy of 93.5 % in the classification process. More granular results revealed a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 4. 2 \%}$</tex> accuracy in identifying lung cancer and 93.0 % for breast cancer, showcasing the model's proficiency in discerning intricate tumor details in MRI imagery. These significant findings have vast implications for the medical field. Such a high degree of accuracy provides a robust foundation for clinicians and radiologists, promoting more informed diagnostic decisions. By potentially streamlining the pathway from early detection to timely therapeutic intervention, this SVM-based approach stands poised to revolutionize cancer treatment strategies. Moreover, as this research amalgamates machine learning with medical imaging, it sets the stage for a future where healthcare is not only more efficient but also inherently patient-focused. This study's outcomes underscore the promise that machine-augmented diagnostics holds, paving the way for advancements in personalized oncology care.