Mostakim, MoinReza, Farhana2026-01-192026-01-1920252025-01ID 16301025http://hdl.handle.net/10361/27460Cataloged from PDF version of thesis.Includes bibliographical references (pages 20-21).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.Uncertainty estimation is crucial for improving the reliability and robustness of image classification systems, particularly in safety-critical domains such as healthcare and autonomous driving. In this work, we propose a novel Enhanced Multi-Head Evidential Fusion (MCEF) framework that leverages multi-head evidential networks, meta-calibration, and attention-based fusion to provide comprehensive uncertainty quantification while improving predictive accuracy. Our method integrates diverse evidence heads with adaptive calibration and uncertainty-guided attention to capture both aleatoric and epistemic uncertainties, as well as head-level disagreement, enabling more reliable confidence estimation. We validate the proposed method on standard benchmark datasets, including CIFAR- 10, MNIST, and Fashion-MNIST, demonstrating that it consistently outperforms existing relevant methods in terms of classification accuracy while providing rich uncertainty decomposition. The proposed approach not only advances the state of uncertainty-aware image classification but also provides a robust foundation for reliable deployment in real-world applications where accurate predictions and uncertainty awareness are critical.30 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Uncertainty estimationEvidential deep learningMulti-head evidential fusionMCEFMeta-calibrationAttention-based fusionEpistemic uncertaintyAleatoric uncertaintyMeasurement uncertainty (Statistics).Image processing.Pattern recognition.Deep learning (Machine learning).Attention--Computer simulation.Thesis