METHOD OF DOMAIN KNOWLEDGE INTEGRATION IN MULTI-STRATEGIC CLASSIFICATION OF MEDICAL IMAGES

Abstract

The integration of domain-specific knowledge into medical image classification systems presents a promising approach to enhance the accuracy and interpretability of diagnostic models, particularly in the field of medical imaging. This study aimed to evaluate the efficacy of incorporating domain knowledge into multi-strategic classification algorithms for medical images, with a focus on magnetic resonance imaging (MRI) of the brain. This was a comparative, explorative study utilizing a newly developed multi-strategic classification framework that integrates medical domain knowledge into the classification process. The dataset comprised MRI scans of the brain, collected from three different medical institutions, totaling 320 images, which were annotated by expert radiologists. The framework employed basic classifiers, where each classifier was trained on examples incorrectly classified by another, fostering a collaborative learning environment. Performance was compared against traditional single-strategy classification methods. The integrated multi-strategic classification model demonstrated superior performance, achieving higher sensitivity, specificity, and accuracy with 98 %, 97 %, and 96 %, respectively. Furthermore, the model significantly improved the interpretability of diagnostic predictions, aligning closely with expert radiologists' assessments. The incorporation of domain-specific knowledge into the classification of medical images significantly enhances both the accuracy and the interpretability of the results. The multi-strategic approach allows for a more robust and nuanced understanding of complex medical images, such as MRI scans of the brain, potentially leading to improved diagnostic processes and outcomes in clinical settings. Future research should focus on expanding the types of medical images analyzed and further refining the integration techniques to encompass a broader spectrum of domain knowledge.