Revolutionizing Medical Imaging: Automated Lung Dissection Using Deep Incremental Learning

Different clinical applications rely on automated lung tissue dissection in chest CT scans to perform disease diagnosis and treatment design activities. The research introduces a modern deep incremental learning (DIL) system to manage the complex challenges which appear during lung tissue dissection work. Exceptional tissue dissection results are achieved through a neural network structure that integrates masked RCNN+UNet for tissue dissection and Generative Adversarial Network+VARMA for classification. The lung dissection structure persists as complex because both its form displays irregularities and its internal organization remains intricate. Obtaining precise dissections is extremely tough to accomplish. The combination of masked RCNN+UNet controls the irregular shapes while achieving better accuracy levels in lung dissection procedures. The inclusion of DIL allows the system to keep improved characteristics from segmented photos. The employed framework provides superior performance assessed through the evaluative Dice Similarity Coefficient (DSC) and Jaccard Index (JI) and mean absolute error (MAE) metrics. Experimental findings show that the current framework surpasses all current state-of-the-art techniques designed for lung dissection. The framework demonstrates high accuracy in lung tissue detection because its DSC score reaches 0.96 while its JI score stands at 0.93 and its MAE value remains below 0.01. Medical image analysis in healthcare will receive advantages from an improved feature extraction method.