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In recent years, besides the medical treatment methods in medical field,Computer Aided Diagnosis (CAD) systems which can facilitate the decision makingphase of the physician and can detect the disease at an early stage havestarted to be used frequently. The diagnosis of Idiopathic Pulmonary Fibrosis(IPF) disease by using CAD systems is very important in that it can be followedby doctors and radiologists. It has become possible to diagnose and follow upthe disease with the help of CAD systems by the development of high resolutioncomputed imaging scanners and increasing size of computation power. The purposeof this project is to design a tool that will help specialists diagnose andfollow up the IPF disease by identifying areas of honeycombing and ground glasspatterns in High Resolution Computed Tomography (HRCT) lung images. Creating aprogram module that segments the lung pair and creating a self-learner deeplearning model from given Computed Tomography (CT) images for the specificdiseased regions thanks to doctors are the main purposes of this work. Throughthe created model, program module will be able to find special regions in givennew CT images. In this study, the performance of lung segmentation was testedby the S{\o}rensen-Dice coefficient method and the mean performance wasmeasured as 90.7%, testing of the created model was performed with data notused in the training stage of the CNN network, and the average performance wasmeasured as 87.8% for healthy regions, 73.3% for ground-glass areas and 69.1%for honeycombing zones.

Finding and Following of Honeycombing Regions in Computed Tomography Lung Images by Deep Learning