Pulmonary Edema and Pleural Effusion Detection Using EfficientNet-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data augmentation technique. The classification task employs a deep learning model based on the EfficientNet-V1-B4 architecture and is trained using the AdamW optimizer. The proposed multiclass system achieved an accuracy (ACC) of 98.3%, recall of 98.3%, precision of 98.7%, and F1-score of 98.7%. Moreover, the robustness of the model was revealed by the Receiver Operating Characteristic (ROC) analysis, which demonstrated an Area Under the Curve (AUC) of 1.00 for edema and normal cases and 0.99 for effusion. The experimental results demonstrate the superiority of the proposed multi-class system, which has the potential to assist clinicians in timely and accurate diagnosis, leading to improved patient outcomes. Notably, ablation-CAM visualization at the last convolutional layer portrayed further enhanced diagnostic capabilities with heat maps on X-ray images, which will aid clinicians in interpreting and localizing abnormalities more effectively.