A novel approach for detection of Lung Cancer using Digital Image Processing and Convolution Neural Networks

Biomedical Image Processing is the latest emerging tool in medical research used for the early detection of cancers. Artificial Intelligence can be used in the medical field to diagnose diseases at an early stage. Computed Tomography (CT Scans) of lungs of the patients from Lung Image Database Consortium (LIDC) is used as input data for image processing. In pre-processing stage conversion of RGB image to gray-scale image takes place because RGB images are too complex to process. Gray-scale image is further converted to Binary image. After Image Processing, the input images become more efficient and refined. These are input for the Convolution Neural Network. Convolution Filtering, Max Pooling filtering are steps in CNN which train the data to predict whether lung image is cancerous (malignant) or non-cancerous (benign). Deep Learning is a newer branch of Artificial Intelligence research will help in better performance in CNN based systems. The proposed system will also take into account the processing power and time delay of the cancer detection process for efficiency.