Detection and diagnosis of lung cancer using region based convolutional neural network model on computed tomography images

Computed tomography is one of the most sensitive imaging techniques for the segmentation of lung cancer. Since lung cancer has the high mortality rate among all the cancers so it becomes so important to detect and diagnose the cancer at beginning stage itself. Segmentation is an important step in medical image analysis and classification for radiological evaluation or computer aided diagnosis. The CAD (Computer Aided Diagnosis) of lung CT generally first segment the area of interest (lung) and then analyse the separately obtained area for nodule detection in order to diagnosis the disease. In this work, implemented a semantic segmentation method using region based convolutional neural network in order to segment the lung nodule part separately from the lung which will enhance the chances of detection of lung cancer and will optimize the extraction of candidate nodules. R-CNN is developed to be used to segment lung regions. The network consists of total 4 convolution layers and 2 max pooling layers. This network has provided an accuracy of 98.28%