Enhancing Diagnostic Accuracy: Leveraging Deep Transfer Learning for Disease Detection in Chest X-Ray Images

Abstract

Chest X-rays are one of the most often used diagnostic instruments in the turf of medicinal imaging, which plays a critical part in the primary detection and diagnosis of disorders. However, radiologists' knowledge is crucial for accurately detecting diseases from chest X-rays, which might lead to inconsistent diagnostic results. This work investigates the use of deep transfer learning to improve chest X-ray image analysis diagnosis accuracy in order to meet this difficulty. In several image identification tasks, deep learning—a subdivision of artificial intelligence—has exposed impressive performance. Because they can automatically train hierarchical feature representations from unprocessed picture input, convolutional neural networks (CNNs) in precise have gained widespread adoption. Deep learning models consume great latent, but creating them from scratch can be quite labour-intensive and time-consuming, especially when working with large quantities of labelled data. A possible remedy is transfer learning, a method that uses big datasets to refine pre-trained models for particular tasks. We can achieve great diagnosis performance with limited labelled medical data by using pre-trained CNN models, like MobileNet and Inception V3, and tailoring them to chest X-ray datasets. Due to their well-known efficient architecture, these models are very accurate and may be deployed in contexts with limited resources. The present study showcases the utilization of MobileNet and Inception V3 for the identification of many illnesses in chest X-ray pictures, such as lung cancer, pneumonia, and TB. We deliver a detailed estimation of the models' routine, contrasting it with conventional diagnostic techniques and pointing out notable gains in sensitivity and accuracy. According to the findings, deep transfer learning with MobileNet and Inception V3 can improve diagnosis accuracy significantly, giving radiologists strong tools and promoting early disease identification. We also go through the ramifications of this technology in clinical settings, how it can lower diagnostic errors, and how AI will be integrated into medical imaging workflows in the future. This study highlights how deep transfer learning with MobileNet and Inception V3 can revolutionize medical diagnosis and open the door to more dependable and effective healthcare systems. Our goal is to enhance patient outcomes and assist medical professionals in providing high-quality care by utilizing cutting-edge AI approaches.