Effect of Hybrid Activation and Loss Functions for Pneumonia Classification in Chest X-ray Images

OpenAlex English

Pneumonia is one of the major infectious diseases leading to death worldwide and its early detection is crucial for successful treatment. Chest X-ray images are a frequently used method for the detection of pneumonia and often contain complex structures to make an accurate diagnosis. In this study, deep learning based models are used to classify normal and pneumonia labeled data in Chest X-ray images. As a result of the comparisons made on MobileNetV2, ResNet50, VGG19, Xception and ViT models, the VGG19 model achieved the highest success with an accuracy of 88.14%. In addition, the proposed hybrid activation function integrated into the VGG19 model performed the best with 91.67% accuracy and improved the classification success. Performance evaluations with the integration of different loss functions (MSE, MAE, Binary Cross-Entropy and the proposed loss function) also revealed that the Proposed Hybrid loss function achieved the highest performance with 92.63% accuracy. These findings show that hybrid activation and loss functions significantly improve classification accuracy in deep learning-based medical imaging applications.