Refined Deep Convolutional Neural Network for Lung Nodule Segmentation in Computed Tomography Histopathological Scans

One of the most deadly types of cancer is lung cancer. In the field of medicine, the creation of a dependable and automated method for separating lung nodules from computed tomography (CT) scans is extremely important. An Optimized Deep Convolutional Neural Network (ODCNet) framework is presented in this study with the goal of improving lung nodule segmentation accuracy. Our methodology makes use of multiple essential elements: (i) a simple thresholding algorithm to reduce noise and artifacts in skin lesion images; (ii) the use of a deep convolutional neural network (DCNN) to precisely segment each pixel within lung images; and (iii) the incorporation of the Adaptive Moment (Adam) optimizer to enhance the segmentation efficiency of the suggested algorithm. By carefully assessing the real-world dataset.