In medical imaging, radiographic diagnoses based on computational techniques and statistical analysis call for a solution that integrates the gap between computational methods and statistical tools to ensure the correctness and reliability of the diagnosis process. Such a gap led to the innovation of a new framework called the “Statistically Improved Convolutional Interpretation (SICI)” framework for transparent and reliable diagnosis of disease from medical images. SICI framework is explored based on Convolutional Neural Networks (CNNs), attention mechanisms, and Explainable artificial intelligence (XAI) and complements it with statistical models. The foundation of this approach is based on the statistical compilation of improvements in the visual inspection accuracy and readability through combining CNNs classes of pathologic features, attention mechanism modules for prioritization of most image-important regions, and XAI elements for bringing statistically significant transparency of diagnostic outcomes. By integrating the statistical analysis into the framework, we aim to rigorously perform experiment research to identify accuracy, efficiency, and interpretability as the performance metrics. Compared to the older methods, it is associated with a marked reduction in error, and increased predictive value of diagnostic tests; thus, tends to bring interpretability and accuracy to the realm of medical image analysis.