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تشخیص توده‌های نرمال و غیر نرمال در تصاویر ماموگرافی توسط شبکه عصبی کانولوشنی عمیق (DCNN) | ||
| مدل سازی در مهندسی | ||
| دوره 22، شماره 79، دی 1403، صفحه 29-43 اصل مقاله (1 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.31040.2479 | ||
| نویسندگان | ||
| فرناز حسینی* ؛ حامد سپهرزاده | ||
| گروه مهندسی کامپیوتر، دانشگاه ملی مهارت، تهران، ایران | ||
| تاریخ دریافت: 08 تیر 1402، تاریخ بازنگری: 24 اردیبهشت 1403، تاریخ پذیرش: 29 اردیبهشت 1403 | ||
| چکیده | ||
| یکی از مهمترین و موثرترین راه‌های تشخیص سرطان سینه، بخصوص در مراحل اولیه بیماری، انجام ماموگرافی است. تصاویر ماموگرافی به علت پیچیدگی بافت‌های سینه، شباهت بین توده‌های سرطانی و بافت‌های طبیعیِ آن، اندازه و شکل متفاوت توده‌ها و تابش اشعه ایکس، معمولا از کیفیت پایینی برخوردار هستند. از این رو تشخیص ضایعات به خصوص در مراحل اولیه، کار بسیار دشواری است؛ زیرا برخی از ضایعات جرم در بافت‌های طبیعی جاسازی شده و حاشیه‌های ضعیف یا حاشیه‌های مبهم دارند. روش پیشنهادی در این مطالعه ارائه‌ی یک معماری مبتنی بر شبکه عصبی کانولوشنی عمیق برای تشخیص توده‌های سرطانی در تصاویر ماموگرافی می‌باشد که در نهایت به طبقه بندی توده‌ها به کلاس‌های نرمال و غیر نرمال منجر می‌گردد. آموزش شبکه‌ی پیشنهادی با اصلاح تصاویر در مرحله پیش‌پردازش آغاز می‌شود تا ترسیم دقیق‌تر با وضوح بالاتر بر روی تصاویر انجام شود و در نهایت دقت و حساسیت جداسازی توده از بافت سینه برای تشخیص صحیح بهبود یابد. برای پیاده‌سازی روش پیشنهادی از زبان برنامه‌نویسی پایتون و کتابخانه تنسورفلو در محیط ویندوز استفاده شده است. برای اطمینان از عملکرد روش پیشنهادی روش اعتبارسنجی متقابل استفاده شده و نتایج بدست آمده توسط معیارهای دقت، صحت و حساسیت مورد ارزیابی قرار گرفته است. نتایج بدست آمده با دقت 97.67 % بیانگر بهبود دقت تشخیص و همچنین کاهش هزینه در فرایند تشخیصِ انجام شده می‌باشد. | ||
| کلیدواژه‌ها | ||
| شبکه عصبی کانولوشنی؛ تصاویر ماموگرافی؛ سرطان سینه؛ یادگیری عمیق؛ توده نرمال و غیر نرمال | ||
| عنوان مقاله [English] | ||
| Normal and Abnormal Masses Detection in Mammography Images Using Deep Convolutional Neural Network (DCNN) | ||
| نویسندگان [English] | ||
| Farnaz Hoseini؛ Hamed Sepehrzadeh | ||
| Department of Computer Engineering, National University of Skills (NUS), Tehran, Iran | ||
| چکیده [English] | ||
| One of the most important and influential ways to diagnose breast cancer, especially in the early stages of the disease, is mammography. Mammography images are usually of low quality due to the complexity of breast tissues, the similarity between cancerous masses and normal tissues, the different sizes and shapes of the masses, and X-ray radiation. Therefore, it is very difficult to detect lesions, especially in the early stages; Because some mass lesions are embedded in natural tissues and have weak margins or vague margins. The proposed method in this study is to present an architecture based on a deep convolutional neural network to detect cancerous masses in mammography images, which ultimately leads to classifying the masses into normal and abnormal classes. The training of the proposed network begins with the modification of the images in the pre-processing stage in order to perform more accurate drawings with high resolution on the images and finally to improve the accuracy and sensitivity of separating the mass from the breast tissue for correct diagnosis. Python programming language and TensorFlow library have been used in the Windows environment to implement the proposed method. To ensure the performance of the proposed method, the cross-validation method was used and the obtained results were evaluated by the criteria of precision, accuracy, and sensitivity. The results obtained with an accuracy of 97.67% indicate the improvement of the diagnosis accuracy and the cost reduction in the diagnosis process | ||
| کلیدواژه‌ها [English] | ||
| Separated by semicolons Convolutional Neural Network (CNN), Mammography images, Breast cancer, Deep learning, Normal and abnormal mass | ||
| مراجع | ||
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