Pulmonary nodules detection algorithm based on robust cascade classifier for CT images

Lung cancer has been the deadliest among all other types of cancer. Our purpose is to propose an efficient method to detect the pulmonary nodules from CT images and classify the nodule into either cancerous (Malignant) or non-cancerous (Benign). We achieve this by framing the problem as a constructing classifier task and exploit data in the form of classifier to learn a mapping from raw data to object classification. In particular, we propose a learning method based on a form of cascade classifier which allows learning in a supervised manner, only based on pulmonary nodule image block extracted from the original CT images without access to around-information annotations. In order to validate our approach, we use a synthetic database to mimic the task of detecting pulmonary nodule automatically from CT images - as commonly encountered in automatic detection of medical images applications - and show that classifier can automatically detect pulmonary nodules from the lungs CT images accurately. The method is able to achieve an overall accuracy of 97.01%.