Efficient Lung Cancer Segmentation Using Deep Learning-Based Models

The most hazardous disease the globe is now dealing with is cancerous. It is challenging to find cancerous nodules inside the lungs, although many techniques have been used to do so. Lung cancer segmentation is a process of identifying and isolating lung cancer tissues from medicinal picture like CT or MRI scan images. This process is essential for accurate diagnosis and management planning of lung cancer. Computing techniques can be used to automate and increase the accuracy of lung cancer dissection. Deep Learning (DL) is a popular technique used in medical image analysis. It has become increasingly important in lung cancer segmentation is the main research work nowadays. This study applied three DL approaches like U-Net, V-Net and the Mask R-CNN for lung cancer separation. Among the three techniques, the U-Net model provides better outcomes based on their evaluation metrics like Accuracy, Sensitivity and Specificity. From the results obtained the proposed U Net gives accuracy of about 97% to 98.4%, Sensitivity of about 88.3% to 91% and Specificity of about 93.2% to 94.6% respectively. The tool used for execution is Matlab.