Publication: Disease detection using deep learning slgorithms on the hardware platforms
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Date
2023-01-01
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Abstract
Covid-19 virus, which emerged at the end of 2019, brought human life to a standstill all over the world, causing many people to become permanently ill and die. Since its emergence, the health system has come to the point of collapse with its rapid spread all over the world. Despite the uninterrupted work of healthcare professionals and fighting with their whole selves, this virus spreaded rapidly and infected many people in the world and caused death. Covid-19 virus also caused permanent lung damage in some of the people who survived this disease. In this article, an answer is sought to detect the virus that causes Covid-19 disease by using machine learning methods. The aim of the study is to detect the Covid-19 disease quickly and to start the treatment process immediately. In this work, different models were designed using X-Ray images of patients with and without Covid-19 disease, and among these models, the most accurate and fastest result was proposed. In this sense, sample data were produced from existing data by applying Zoom Range, Shear Range and Horizontal Flip data augmentation methods, since data on Covid-19 is not much. In addition, improvements were made using CNN, VGG16, DenseNet121 and ResNet50 deep learning methods to design proposed model. Since the main aim of the study is to achieve the highest accuracy rate quickly, the performances of deep learning algorithms in different working environments were evaluated. CPU, GPU and TPU are used for this. As a result of experimental studies, it has been observed that all algorithms working with GPU work faster with or without data augmentation. In addition, although deep learning algorithms have been successful in working with big data, it has been seen in this study that there is no need for data augmentation for Covid-19 disease detection such a dataset. By examining such image data on the GPU with any deep learning algorithm proposed in this study, we can detect the disease successfully and quickly.
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Tıp, Sosyal ve Beşeri Bilimler, Sosyoloji, Kütüphanecilik, Tarımsal Bilimler, Ziraat, Tarım Makineleri, Tarımda Enerji, Biyoyakıt Teknolojisi, Bilgisayar Bilimleri, Algoritmalar, Sağlık Bilimleri, Temel Tıp Bilimleri, Biyoistatistik ve Tıp Bilişimi, Mühendislik ve Teknoloji, Medicine, Social Sciences and Humanities, Sociology, Library Sciences, Agricultural Sciences, Agriculture, Farm Machinery, Energy in Agriculture, Biofuels Technology, Computer Sciences, algorithms, Health Sciences, Fundamental Medical Sciences, Biostatistics and Medical Informatics, Engineering and Technology, Klinik Tıp (MED), Mühendislik, Bilişim ve Teknoloji (ENG), Sosyal Bilimler (SOC), Klinik Tıp, Bilgisayar Bilimi, Mühendislik, Sosyal Bilimler Genel, TIBBİ BİLİŞİM, BİLGİSAYAR BİLİMİ, YAPAY ZEKA, ENERJİ VE YAKITLAR, BİLGİ BİLİMİ VE KÜTÜPHANE BİLİMİ, Clinical Medicine (MED), Engineering, Computing & Technology (ENG), Social Sciences (SOC), CLINICAL MEDICINE, COMPUTER SCIENCE, ENGINEERING, SOCIAL SCIENCES, GENERAL, MEDICAL INFORMATICS, COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE, ENERGY & FUELS, INFORMATION SCIENCE & LIBRARY SCIENCE, Yapay Zeka, Fizik Bilimleri, Bilgisayar Bilimi Uygulamaları, Bilgisayarla Görme ve Örüntü Tanıma, Bilgi Sistemleri ve Yönetimi, Sosyal Bilimler ve Beşeri Bilimler, Enerji Mühendisliği ve Güç Teknolojisi, Tıbbi Bilişim, Artificial Intelligence, Physical Sciences, Computer Science Applications, Computer Vision and Pattern Recognition, Information Systems and Management, Social Sciences & Humanities, Energy Engineering and Power Technology, Health Informatics, component, formatting, insert, style, styling
Citation
KARATAŞ BAYDOĞMUŞ G., Cicekli N. Z., \"Disease Detection using Deep Learning Algorithms on the Hardware Platforms\", 2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023, Sivas, Türkiye, 11 - 13 Ekim 2023