Enhancing Lung Image Segmentation Using Hybrid U-Net and Transfer Learning: A Comprehensive Approach on Tuberculosis Diagnosis

Tuberculosis (TB) stands as the foremost global cause of mortality, a highly contagious lung ailment trailed closely by malaria and HIV/AIDS. To facilitate precise lung X-ray image analysis, which is crucial for diagnoses like lung tuberculosis, lung X-ray image segmentation takes precedence. The formidable U-net architecture, renowned in deep learning for image segmentation, is prominent in this endeavor. This architectural marvel comprises a contracting pathway, adept at extracting high-level information, and a symmetrically expanding pathway, adept at restoring vital details. Setting itself apart, this network outshines many counterparts and exhibits the capacity for comprehensive training even with a limited dataset. In this context, the primary goal of our work is to provide an automated lung segmentation method aimed at addressing the challenge of reconstructing damaged lung sections, which makes a significant contribution to our field of medical science and in the application of artificial intelligence that automatically segments an image of the lung to aid TB detection and classification. The proposed approach can be distilled into three fundamental steps: (a)Image Acquisition: This initial step involves describing the materials and techniques employed for image collection. (b)Initial Segmentation: This critical phase utilizes the power of the U-net deep convolutional network (CNN) model and employs three distinct approaches. These approaches play a pivotal role in the initial segmentation of lung regions. (c) Ensemble Modeling: Subsequently, all three models are amalgamated through ensemble modeling. This consolidation process combines the best outputs from all three right at every pixel in each of the three approaches to yield a final result. In summary, our focus is achieving accurate lung segmentation, particularly for damaged sections, using a comprehensive method that leverages semantic segmentation, transfer learning, and deep learning techniques, notably the Hybrid U-Net model. This approach also enhances lung nodule detection, making it a valuable contribution to the field. Three transfer learning methods trained on large-size image datasets are ResNet34, Inception V3, and VGG 16. The results demonstrated impressive performance metrics, including Mean IoU, Dice-Score, and F-Score. The result section shows that the hybrid U-net model with inception V3 gives better results than the rest, with 0.975 Mean IoU, 0.987 Dice Score, and 0.9963 F-Score. After the ensemble method improved, the result of (0.976 )IoU and (0.988) Dice-Score was achieved.