Probabilistic Image Analysis for Chest X-Ray Classification: Integrating Density Functions as Descriptive Features - Elias Polus, Manhal (Al-Nahrain Journal of Science, 2025)
In this study, we present a technique for chest X-ray image classification by integrating Generalized Extreme Value (GEV) probability density functions (PDFs) with Convolutional Neural Networks (CNNs). The traditional method is related to our proposed method, and the results show significant enhancements in key performance metrics. The proposed method reaches an accuracy of 91.95%, higher than the traditional method's 89.85%, typically enhanced capabilities in accurate classification. Precision is higher in the proposed method at 91.13%, emphasizing its proficiency in correctly identifying positive cases. Specificity is improved in the proposed method (67.22%) compared to the traditional method (61.20%), which represents a lower risk of false positives. The F1-score of 95.01% in the proposed method indicates a consistent balance between precision and recall, underlining its effectiveness in minimizing both false positives and false negatives. These findings suggest that the integration of GEV PDFs and CNNs holds potential for progressing chest X-ray image classification accuracy and reliability, with possible suggestions for improving diagnostic procedures in clinical settings.