LDLCT an instance-based framework for lesion detection on lung CT scans

Medical images have played a crucial role in transforming diagnostic medicine by providing the medical staff with several insights into the health status of every patient. Diagnosis of medical images is a very subjective process which is solely based on the physicians' expertise. In particular, lesion detection is a challenging task due to the various types, shapes and sizes of the lesions in the different organs. Thus, there is a crucial need to build computer-aided frameworks for automated analysis of medical images. In this paper, we present LDLCT, an instance-based framework for automated Lesion Detection on Lung CT Scans. The framework employs Restricted Botlzman Machines (RBM) network for feature extraction stage as an unsupervised feature mapping. The Random Forest (RF) classifier is used to distinguish between pixels from lesion and normal regions. Finally, a post-processing stage is implemented to filter out the false positive candidate lesions. In this study, we select 909 slices with 917 lesions from DeepLesion data set. LDLCT achieves lesion detection sensitivity of 89% with 5 false positives per image, the majority of them can be easily detected by the medical staff.