Analysis of Lung Cells with a Novel Segmentation Methodology Using FCN and Deeplab V3

One of the primary causes of death globally, cancer is estimated to have affected more people in less developed nations than in more developed ones throughout time in terms of both cases and mortality. The unchecked growth of abnormal cells that begins in one or both of the lungs is known as lung cancer. The tumors impede the lungs' capacity to provide oxygen to the circulation as they enlarge. Medical images are analyzed using image processing, machine learning, and other technologies to help in early illness identification and treatment. When it comes to diseases like cancer, time is of the essence and should be investigated as soon as feasible. To diagnose lung cancer, we suggested a novel hybrid segmentation technique in this work. Using a fully convolutional network (FCN) and DeeplabV3,we were able to separate lung cancer cells hybridly. According to the findings, our approach performs well and is very accurate when compared to other currently used approaches. The proposed hybrid FCN with the Deeplabv3 model segments the input lung image with foreground and background with 93% and 97% of accuracy. And also shows 86% of IOU and 93% of the score showing the system performs well in the lung cancer segmentation process.