Synergistic Fusion of U-Net and DeepLabV3+ for Enhanced Lung Disease Segmentation: A Comprehensive Evaluation and Performance Analysis

Unet and DeepLabV3+ join forces in the realm of lung disease screening. Unet’s knack for identifying tricky features pairs up with DeepLabV3+’s extensive view, enhancing our fight against lung disease. What we get is better exactness in identifying and mapping these illnesses. Doctors can now spot disease early and plan the perfect treatment. Sure, it needs a lot of data and processing power, but it’s a small price for the big leap in lung disease detection. This blend of Unet and DeepLabV3+ propels us to the future of disease diagnosis, marking a powerful union of deep learning and medical imaging tech. Unet model gets it right 97.00% of the time when classifying pixels. The Dice and Jaccard index, 90.8% and 83.31% respectively, prove decent job at spotting tuberculosis-related issues. Yet, a 0.9 loss points at some hiccups during training. DeepLabV3+, on the other hand, outdoes Unet with a whopping 98.27% accuracy. Its Dice score and Jaccard index of 98.18% and 96.43% respectively, showcase great potential in identifying the finer details essential for tuberculosis X-ray segmentation. DeepLabV3+ delivers excellent results due to a low loss of 0.14. This shows its strong learning ability during training. In all areas, this tool stands out. Especially, it shines in X-ray analysis of tuberculosis, a challenging task. This quality makes it a strong aid for advancing the study of medical pictures.