بهبود کاهش نویز، تقطیع و طبقه‌بندی توده‌های سرطان توسط فیلتر تطبیقی معکوس کوانتومی، عنکبوت اجتماعی و ELM بهبود یافته

افتخاریان, محسن; نودهی, علی; عنایتی فر, رسول

doi:10.22052/scj.2022.243230.1018

بهبود کاهش نویز، تقطیع و طبقه‌بندی توده‌های سرطان توسط فیلتر تطبیقی معکوس کوانتومی، عنکبوت اجتماعی و ELM بهبود یافته

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ گروه کامپیوتر، واحد گرگان، دانشگاه آزاداسلامی، گرگان، ایران.

² گروه کامپیوتر، واحد فیروزکوه، دانشگاه آزاد اسلامی، فیروزکوه، ایران.

10.22052/scj.2022.243230.1018

چکیده

سیستم‌های تشخیص هوشمند پزشکی امروزه به لطف هوش مصنوعی، دچار تغییرات و همچنین چالش‌هایی شده‌اند. یکی از این سیستم‌های هوشمند پزشکی، سیستم‌های تشخیص و طبقه‌بندی توده‌های سرطانی از نواحی سینه می‌باشد. تشخیص زودهنگام می‌تواند منجر به افزایش گزینه‌های درمانی شود. انواع تکنیک‌های غربالگری برای سرطان سینه مانند ماموگرافی، MRI و التراساند وجود دارد. بسته به روش تشخیص نوع توده‌های سرطانی، از هرکدام از این تصاویر استفاده ‌شده و تکنیک‌های پردازشی متفاوتی برای آنها ارائه‌شده است. این تحقیق به استفاده از مجموعه داده‌های ماموگرافی MIAS می‌پردازد و بر اساس اصول پردازش تصویر و یادگیری ماشین، سعی در تشخیص و طبقه‌بندی توده‌های خوش‌خیم، بدخیم و مشکوک را دارد. لذا به ارائه یک رویکرد تکامل‌یافته می‌پردازد، بدین‌صورت که در ابتدا عملیات پیش‌پردازش با هدف کاهش نویز و بهسازی تصویر مبتنی بر روش پیشنهادی Quantum Inverse MFT انجام می‌شود و سپس بر اساس شدت روشنایی و لبه، عملیات تقطیع تصویر با استفاده از الگوریتم عنکبوت اجتماعی صورت می‌گیرد. در ادامه عملیات استخراج ویژگی‌ها و طبقه‌بندی با هدف تشخیص نوع توده‌های سرطانی، با روش Extereme Learning Machine و مدل توسعه‌یافته آن یعنی Moore Penrose Matrix - Extereme Learning Machine انجام می‌شود. نتایج تحقیق نشان می‌دهد که رویکرد پیشنهادی از لحاظ معیارهای ارزیابی همچون دقت، حساسیت، نرخ ویژگی‌ها و همین‌طور ROC و AUC نسبت به روش‌های پیشین دارای برتری عملکردی است.

کلیدواژه‌ها

20.1001.1.23223707.1400.10.1.5.5

عنوان مقاله [English]

Improved noise reduction, segmentation, and classification of cancer masses by quantum inverse-matched filter, social spider algorithm, and improved ELM

نویسندگان [English]

Mohsen Eftekharian ¹
Ali Nodehi ¹
Rasul Enayatifar ²

¹ Computer Department, Gorgan Branch, Islamic Azad University, Gorgan, Iran.

² Computer Department, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.

چکیده [English]

Intelligent medical diagnosis systems have been revolutionized by artificial intelligence, leading to new challenges and opportunities. One such system is the classification and diagnosis of breast cancer masses, which can benefit from early detection and increased treatment options. Various screening techniques are available for breast cancer, including mammography, MRI, and ultrasound, each requiring different images and image processing techniques depending on the type of cancer mass diagnosis. This paper focuses on using the MIAS mammography dataset to diagnose and classify benign, malignant, and suspicious masses based on image processing and machine learning techniques. To achieve this goal, a developed approach is proposed, in which preprocessing operations are performed at first to reduce noise and enhance the image based on the proposed Quantum Inverse MFT method, and then image segmentation is applied using the social spider algorithm based on brightness intensity and edge. Subsequently, feature extraction and classification operations are performed using the Extreme Learning Machine method and its developed model, i.e., Moore Penrose Matrix-Extreme Learning Machine, with the aim of diagnosing the type of cancer mass. The results show that the proposed approach has a higher accuracy rate than other methods in terms of some evaluation criteria such as accuracy, sensitivity, specificity and also ROC and AUC.

کلیدواژه‌ها [English]

Breast cancer
Diagnosis and classification
Social spider algorithm
Quantum inverse MFT
MPM-ELM model

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