Abstract / Summary
Abstract CWTRNet: A Class weighted TernausResnet framework with adaptive optimization is proposed in this work for high reliability medical image classification. Deep learning(DL) models have made significant advancements in the analysis of medical images, especially in diagnosis and classification. TernausNet, on the other hand, is mostly intended for semantic segmentation rather than classification, yet it successfully maintains multi-scale spatial information using encoder–decoder skip connections. This research gap motivates the creation of a TernausNet adaptation focused on classification that takes the benefit of multi-scale feature refinement without requiring dense segmentation outputs. In order to address class imbalance in image classification, the customized TernausResnet and class-weighting is used in this experimental study. Additionally, training incorporates class-weighted learning to increase minority-class recognition and lessen the impact of class imbalance. The pretrained Resnet-50 backbone offers strong feature representations, which are fine-tuned on medical datasets to enhance diagnostic accuracy. The proposed model was assessed using the F1-score, Geometric Mean, Matthews Correlation Coefficient (MCC), Cohen’s Kappa (CK), and Area Under the Curve (AUC) on chest X-ray dataset under identical learning configurations. The model was evaluated with various medical images such as PneumoniaMNIST and OrganAMNIST and the experimental findings indicate that the CWTRNet achieves high classification accuracy and robust generalization. The proposed model not only enhances classification performance but also improves interpretability by highlighting the key areas affecting the model predictions through Grad-CAM visualization. The model achieves 98.8% training accuracy with a test accuracy of 98.74% and a test loss of 0.0418. The proposed model demonstrated good generalization and low prediction error. Strong discriminative capacity is shown in the classification report for all the classes. The proposed model has been evaluated with MCC and CK metrics for demonstrating per class accuracy metrics. The suggested model performs well per class, as seen by its strong agreement with Cohen’s Kappa = 0.98 and Matthew’s Correlation Coefficient = 0.98. These findings demonstrate that a lightweight classification module for the segmentation output head, combining a residual encoder with decoder-based multi-scale feature refinement class-weighted optimization offers an efficient framework for managing imbalanced medical image classification tasks.