In this project a novel procedure to apply deep learning techniques to medical image classification was proposed. With increasing popularity of ?Chest X rays, fully manual diagnosis of lung nodules puts a burden on the radiologists who need to spend hours reading through Lung nodule images to identify Region of Interests (ROIs) to schedule follow-ups.
Accurate computer-aided diagnosis of lung cancer can effectively reduce their workload and help training new radiologists. However, lung nodule detection is challenging because of the varying size, location, shape, and density of nodules.
Many studies have approached this problem using image-processing techniques with the intention of developing an optimal set of features.
Convolution neural network has demonstrated to learn discriminating?visual features automatically and has beat many state-of-art algorithms in image-processing tasks, such as pattern recognition, object detection, segmentation, etc. In this report, we evaluate the feasibility of implementing deep learning algorithms for lung cancer diagnosis with the Lung Image Database Consortium (LIDC) database.. The performance of our best model is comparable with the state-of-art results in the lung nodule detection task.