Revolutionizing Covid-۱۹ Diagnosis: The Impact of Automated Chest X-ray Analysis through Deep Learning
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 69
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شناسه ملی سند علمی:
ITCT20_096
تاریخ نمایه سازی: 5 مهر 1402
چکیده مقاله:
Using cutting-edge technology, this groundbreaking study developed a novel approach to diagnosing COVID-۱۹. By utilizing wavelet transformation and fuzzy logic, we have successfully removed noise from CT images, enabling us to accurately segment lung regions. Our innovative approach combines global and local threshold methods, resulting in unparalleled success in segmenting lung images. We have further employed state-of-the-art techniques such as AlexNet for feature extraction and Support Vector Machine (SVM) for classification, achieving an astonishing ۹۹.۸% accuracy in classifying COVID-۱۹, Viral Pneumonia, and Normal data. Our method outperforms previous approaches and represents a significant breakthrough in medical diagnosis.
کلیدواژه ها:
Convolutional neural networks ، COVID-۱۹ ، AlexNet ، Support vector machine (SVM) ، lung segmentation.
نویسندگان
Zahra Khodakaramimaghsoud
Computer Engineering, University of Isfahan, Isfahan, Iran
Sara yousefi Javan
Computer Engineering, Islamic Azad University of Mashhad, Mashhad, Iran