Lung Nodule Detection Interface Design and Development From Computerized Tomography Images
Abstract
Lung cancer is one of the leading diseases that cause death in the world. Early diagnosis of lung cancer is as important as its treatment. Therefore, we propose the LinkNet architecture, which is a deep learning model that will detect the location and size of nodules from the lung tomography image. The study was conducted with 110 patients and 343 nodules with nodules detected in Lung Computed Tomography (CT) Images. In the study, no public dataset was used and tomography images were obtained from the hospital. In the pre-processing stage, thresholding is made according to the lung Hounsfield Unit (HU) threshold value with the Otsu method and the lung is segmented. The XML (Extensible Markup Language) files of ROIs(Region of Interest) of the nodules previously marked by the radiologist are extracted and converted into images. Using template nodules trained with LinkNet and U-Net architectures, comparison and success percentage tables for 16 different architectures were presented. Using LinkNet, the model achieved an intersection of union (IoU) score of 69% for valid and an IoU score of 94% for the train. 274 nodule data were used in the train section and 69 nodule data were used in the validation section. Experimental results show that nodules that may be overlooked by a radiologist can be detected with CAD (Computer- Aided Design) performed and will be useful in the diagnosis of lung cancer.
Keywords
Lung Nodule Detection, CT Image, Otsu Thresholding, U-Net, LinkNet
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