دوره 20، شماره 3 - ( 10-1402 )                   جلد 20 شماره 3 صفحات 46-27 | برگشت به فهرست نسخه ها


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Mohammadian Takaloo V, Hashemzadeh M, Ghavidel Neycharan J. CoviX-Net: A Deep Learning-based System for Diagnosis and Differentiation of Covid-19 Infection and Pneumonia in Chest Radiography Images. JSDP 2023; 20 (3) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1238-fa.html
محمدیان وحید، هاشم زاده مهدی، قویدل نیچران جلیل. CoviX-Net: سامانه مبتنی بر یادگیری عمیق برای تشخیص و تمایز عفونت کوید-19 و ذات‌الریه در تصاویر رادیوگرافی قفسه سینه. پردازش علائم و داده‌ها. 1402; 20 (3) :27-46

URL: http://jsdp.rcisp.ac.ir/article-1-1238-fa.html


دانشگاه شهید مدنی آذربایجان
چکیده:   (632 مشاهده)
در این پژوهش، سامانه CoviX-Net مبتنی بر یادگیری عمیق برای تشخیص و تمایز بیماری کوید-19 و انواع ذات‌الریه از روی تصاویر رادیوگرافی سینه ارائه می‌شود. معماری مدل یادگیری CoviX-Net، بر اساس معماری اِکسپشن چند لایه و متناسب با کاربرد مورد هدف طراحی شده است. در این سامانه، از یادگیری انتقالی برای رفع مشکل کمبود داده آموزشی استفاده می‌شود. همچنین برای فراهم نمودن داده آموزشی کافی، یک پایگاه تصاویر جامع با بهره‌گیری مناسب از دو منبع مختلف از تصاویر قفسه سینه ایجاد شده است. برای جلوگیری از مشکل بیش‌برازش، فنون افزایش داده، تَنزلِ وزن و تنظیم ‌کننده‌های L2 استفاده شده است. نتایج ارزیابی‌ها نشان می‌دهد دقت CoviX-Net در حالت سه طبقه (کوید-19، ذات‌الریه و ریه طبیعی) %25/99، و در حالت چهار طبقه (کوید-19، ذات‌الریه باکتریایی، ذات‌الریه ویروسی و ریه طبیعی) %95 است که در مقایسه با دیگر روش‌های طبقه‌بندی مبتنی بر یادگیری عمیق با ساختار مشابه، بهبود دقت %5 و در مقایسه با روش مبتنی بر یادگیری عمیق انتقالی موازی، با ساختار پیچیده، بهبود دقت حدود نیم درصد را دارد. کلیه کدهای پیاده‌سازی CoviX-Net و مجموعه تصاویر گردآوری شده در دسترس عموم پژوهشگران قرار گرفته است.
شماره‌ی مقاله: 3
متن کامل [PDF 1983 kb]   (155 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: مقالات پردازش تصویر
دریافت: 1400/3/12 | پذیرش: 1402/9/20 | انتشار: 1402/10/24 | انتشار الکترونیک: 1402/10/24

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