چکیده مقاله
The Covid 19 virus was discovered in Wuhan China in 2019 and spread rapidly throughout the world dueto its high transmission power A timely and accurate diagnosis of Covid 19 is essential to the patient'srecovery Based on deep learning algorithms and CT images, this study proposed hybrid methods todiagnose COVID 19 First, we use wavelet transformation in combination with fuzzy logic to provide anew approach to removing the noise of CT images Then we segmented lung images by the proposedcombined global and local threshold method In this way, lung regions from CT images can be segmentedsuccessfully In the next step, features and classification will be extracted AlexNet is used to extractfeatures, while a Support Vector Machine SVM is used for classification With 99 8% accuracy, threeclasses of data are classified: COVID 19, Viral Pneumonia, and Normal In comparison with previousmethods, the proposed method shows superior classification performance
کلیدواژهها
نویسندگان
Amir Mahdi Jamshidi
Master of Electrical Engineering, Islamic Azad University of Hamedan, Hamedan, Iran
Dorna Nourbakhsh Sabet
Bachelor of Mechanical Engineering, K N Toosi University of Technology, Tehran, Iran
شیوه ارجاع
Jamshidi, Amir Mahdi and Nourbakhsh Sabet, Dorna,1401,Analysing COVID-19 in Medical Images,The 17th National Conference on Electrical, Computer and Mechanical Engineering,Shirvan
ارائهشده در
مجموعه مقالات هجدهمین کنفرانس ملی مهندسی برق، کامپیوتر و مکانیک5 تیر 1402 · شیروان