Refining Chest X-ray Interpretation with Deep Transfer Learning Techniques

Abstract

Chest X-rays are essential diagnostic tools for thoracic diseases, but different doctors' interpretations can differ greatly, which frequently results in inconsistent diagnoses. By using deep transfer learning techniques, this study seeks to improve the accuracy of Chest X-ray interpretations. More accurate and effective analysis can be accomplished with the growing potential of artificial intelligence (AI) in medical imaging, especially through convolutional neural networks (CNNs). It takes a lot of resources to train these models on sizeable annotated datasets, though. By optimising models that have already been trained on general datasets for particular medical imaging tasks, transfer learning provides a solution. In order to improve image quality, this study presents a dual-model framework that makes use of MobileNetV2 and InceptionV3. It is optimised using sophisticated preprocessing techniques like Contrast Limited Adaptive Histogram Equalisation (CLAHE) and white balance correction. Together with these improvements, data augmentation fills in the existing gaps in deployable, lightweight models for real-time applications in clinical settings with limited resources. When tested on illnesses like lung cancer, pneumonia, and tuberculosis, the system demonstrated notable gains in sensitivity and classification accuracy when compared to conventional diagnostic techniques. Additionally, the models show promise for being incorporated into clinical workflows, which would help radiologists detect diseases early and cut down on diagnostic delays. All things considered, this strategy helps provide healthcare in a more reliable, effective, and easily accessible manner.