Özet:

The aim of the study is to detect the abnormal area(s) from brain CTs of stroke patients using Image Processing and to accurately evaluate the stroke changes in brain tissues among patients with Deep Learning models in MATLAB 2019b interface. 1000 patients (500 stroke suspected, 500 healthy participants) were chosen between 25 and 75 age ranges from TOBB ETU and Yıldırım Beyazıt University Hospitals according to the ethics committee certificate. For this study, for increasing the accuracy and eliminating the redundancy, from the image data of the patients, only lateral and 4th ventricle CT images were used. Firstly, these images were processed via Image Processing methods (Image Acquisition, Preprocessing, Thresholding, Segmentation, Morphological Operations etc.). After these methods, the resulted lateral ventricle image was split into 6 specific areas and 4th ventricle image was split into 14 specific areas like automated computerized Alberta Stroke Scoring, respectively. For 1000 images, totally 20x1000=20000 pieces of CT subimages were obtained with the specific class names (as healthy and stroke) and were used as the input of Artificial Intelligence (AI) and Deep Learning (DL) models (optimized ANN with Levenberg-Marquardt method and CNN). This approach can give an important chance to the doctors for supporting their results with a decision support system, speeding up the diagnosis time and also decreasing the possible rate of misdiagnosis.

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