Automated Lung Nodule Segmentation Using an MH-UNet Deep Learning Framework

Resumen

One of the leading causes of abnormal deaths globally is lung cancer, and pertaining to the improvement of patient's prognosis depends directly on early identification. To diagnose such condition the proposed system has identified lung nodule segmentation from CT scan pictures that is robust and effective. The goal of this methodology is to segregate lung nodules from CT scan pictures using a deep learning network called MH-Interposed system using the MH-UNet deep learning model to separate lung nodules from CT scan pictures. To predict lung cancer, the model was trained to identify region of interest of lungs and detect nodules. Proposed system uses 888 CT scan pictures and their corresponding ground truth nodule masks to perform segmentation. Jaccard Index and Dice Score were calculated to evaluate the performance of the system. The model detected with great accuracy in lung nodule detection with an average Dice Score of 0.92 and a Jaccard Index of 0.85.