طبقه‌بندی خودکار تصاویر سرطان پستان با استفاده از یادگیری انتقال بر روی تصاویر ماموگرافی بهبودیافته

چکیده مقاله :

سرطان پستان، یکی از نگرانی‌های مهم در حوزه بهداشت جهانی محسوب می‌شود، که به دو نوع خوش‌خیم و بدخیم تقسیم می‌شود. نوع بدخیم آن به دلیل متاستاز سریعتر، خطر بیشتری دارد. از اینرو، نیاز حیاتی به تشخیص سریع و دقیق دارد. علیرغم تخصص رادیولوژیست‌ها، خطاهای ناشی از تفسیر غلط منجر به تشخیص‌های نادرست می‌شود. برای حل این مشکل، این مقاله سیستم هوشمندی را برای تحلیل تصاویر ماموگرافی پیشنهاد می‌کند که شامل مراحل پیش‌پردازش، استخراج ویژگی‌ها و طبقه‌بندی آنها می‌باشد. در این سیستم، ابتدا با استفاده از تکنیک‌های پیش‌پردازشی مانند بهبود هیستوگرام تطبیقی محدود شده توسط کنتراست کیفیت تصویر را بهبود بخشیده و در ادامه از روش قطعه‌بندی به روش آستانه‌گذاری آتسو برای استخراج ناحیه مربوط به توده سرطانی استفاده می‌شود. همچنین با استفاده از دو مدل از پیش آموزش داده شده شبکه‌های عصبی پیچشی (CNN)، یعنی ResNet50 و InceptionV3، ویژگی‌های کلیدی برای تمایز بین تومورهای خوش‌خیم و بدخیم استخراج می‌شود. در نهایت، با بهره‌مندی از طبقه‌بند ماشین بردار پشتیبان (SVM)، ویژگی‌های استخراج شده به منظور پیشبینی نوع تومورها، تجزیه و تحلیل می‌شود. نتیجه این کار، بهبود دقت تشخیص و همچنین تشخیص زودهنگام سرطان پستان است، که منجر به کاهش خطای انسانی و چالش‌های فعلی در تفسیر تصاویر ماموگرافی می‌شود.

چکیده انگلیسی :

Breast cancer is considered one of the major concerns in global health, and it is divided into two types: benign and malignant. The malignant type poses a higher risk due to its faster metastasis. Therefore, there is a critical need for fast and accurate detection. Despite the expertise of radiologists, errors due to incorrect interpretation often lead to misdiagnoses. To address this issue, this paper proposes an intelligent system for analyzing mammography images, which includes preprocessing, feature extraction, and classification stages. In this system, the image quality is first improved using preprocessing techniques like Contrast Limited Adaptive Histogram Equalization (CLAHE), and then the region corresponding to the cancerous mass is extracted using Otsu’s thresholding segmentation method. Additionally, key features for distinguishing between benign and malignant tumors are extracted using two pre-trained Convolutional Neural Network (CNN) models, namely ResNet50 and InceptionV3. Finally, the extracted features are analyzed using a Support Vector Machine (SVM) classifier to predict the tumor types. The result of this work is an improvement in diagnostic accuracy and early breast cancer detection, which reduces human error and the current challenges in interpreting mammography images

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