Chest X-Ray Medical Report Generation Using a CNN—Transformer Model with Maximum Attention

Medical imaging, particularly chest X-rays, plays a vital role in radiological diagnosis. However, interpreting these images and generating detailed diagnostic reports is a time-consuming task for clinicians. To address this challenge, this study proposes an automated image captioning framework for chest X-ray images, aiming to reduce clinical workload and enhance diagnostic efficiency. The proposed approach employs convolutional neural networks (CNNs) for visual feature extraction and a modified Transformer architecture—referred to as the Medical Transformer—for structured report generation. Three CNN models, namely InceptionV3, ResNet152V2, and Inception–ResNetV2, were evaluated as feature extractors. The attention mechanisms, Bahdanau, Luong, and scaled dot product, were activated by ReLU or Tanh functions to identify the optimal configuration, i.e., the maximum attention is used. Experiments were conducted using the Indiana University Chest X-ray dataset, which contains 7466 images paired with corresponding diagnostic reports. The proposed approach employs image augmentation to accommodate input variability, utilizes Inception–ResNetV2 for feature extraction, and integrates the Medical Transformer with maximum attention mechanisms to achieve optimal performance in medical report generation. Evaluation metrics include BLEU (BLEU-1 to BLEU-4 scores of 0.720, 0.669, 0.648, and 0.600, respectively), METEOR (0.741), and BERTScore (FBERT = 0.787), demonstrating superior performance compared to baseline models and the state of the art. These results validate the effectiveness of the proposed Medical Transformer framework in generating accurate and clinically relevant medical image captions.