An Explainable and Uncertainty Aware Hybrid Deep Learning Framework for Prostate MRI Image Classification
Year: 2026
Type: preprint
Abstract: Background Artificial Intelligence systems for prostate Magnetic Resonance Imaging (MRI) require not only strong discrimination but also interpretable outputs and an indication of predictive uncertainty. This study develops an image-level framework that combines engineered MRI features, complementary neural learners, post-hoc explanations, and Monte-Carlo (MC) dropout. Methods DICOM images were represented by 265 features: a 256-bin normalized intensity histogram, five statistical descriptors, and four Gray Level Co-occurrence Matrix (GLCM) descriptors. A Multilayer Perceptron (MLP), one-dimensional Convolutional Neural Network (1D-CNN), and residual dense network produced class probabilities. These six probabilities were concatenated with the original features and processed by a 271-input stacking meta-learner. The supplied experiment used a stratified 60/20/20 training/validation/test split. Performance was assessed with accuracy, precision, sensitivity, F1-score, and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC). LIME and SHAP offered both local and global explanations, and 50 stochastic forward passes estimated MC-dropout uncertainty. Results On the reported 1,200-image test partition, 98.25% of accuracy, 98.23% of F1-score, and 0.9907 of ROC-AUC were achieved by the hybrid model, exceeding the best base-model accuracy by 2.58 percentage points. The confusion matrix contained 596 true-negatives, 4 false-positives, 17 false- negatives, and 583 true-positives, corresponding to 99.32% precision and 97.17% sensitivity. Conclusion Probability-level fusion of heterogeneous learners can improve image-level discrimination while supporting feature attribution and uncertainty inspection. However, the current labels are derived from DICOM study descriptions rather than pathology-confirmed cancer status. The reported performance is therefore methodological and must not be interpreted as clinical cancer-detection performance until the model is rerun with patient-grouped partitions, training-only preprocessing and resampling, verified cancer labels, and independent external validation.
Source: Research Square
Institutions: JIS University, Lovely Professional University, Sharda University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7225829015