تشخیص سرطان سینه با استفاده از شبکه بهبودیافته ZFNet-ELM با الگوریتم بهینه‌سازی COA

خسروی, سارا

doi:10.22052/scj.2025.255463.1269

تشخیص سرطان سینه با استفاده از شبکه بهبودیافته ZFNet-ELM با الگوریتم بهینه‌سازی COA

نوع مقاله : مقاله پژوهشی

نویسنده

سارا خسروی

گروه کامپیوتر ، دانشکده فنی و مهندسی، دانشگاه پیام نور، تهران، ایران

10.22052/scj.2025.255463.1269

چکیده

امروزه بسیاری از محققان با استفاده از سیستم‌های مبتنی بر یادگیری عمیق و هوش مصنوعی، در حوزه پزشکی به تشخیص و طبقه‌بندی بسیاری از بیماری‌ها مانند تشخیص سرطان می‌پردازند. تشخیص زودهنگام و دقیق سرطان سینه بر اساس تصاویر ماموگرافی می‌تواند به پزشکان در درمان به موقع بیماری کمک بسزایی کند. از طرفی اکثر شبکه‌های عمیق مورد استفاده در سیستم‌های تشخیص سرطان سینه (BC)، به دلیل استفاده از لایه‌های تمام متصل (FC)، روش آموزشی زمان بر و حجم محاسبات بالا، برای کاربردهای بلادرنگ مناسب نیستند. لذا در این مقاله، یک روش جدید ترکیبی مبتنی بر یادگیری عمیق و هوش ازدحامی به نام EZFNet-ELM-COA جهت تشخیص بلادرنگ سرطان سینه پیشنهاد شده است. در شبکه پیشنهادی (Enhanced ZFNet)، در لایه‌های کانولوشنی شبکه ZFNet، فیلترهای اندازه بزرگ با دو سری فیلتر اندازه کوچک‌تر جایگزین شده‌اند و سپس خروجی‌های فیلترها با یکدیگر الحاق شده است. استفاده از این تکنیک چندمقیاسی باعث استخراج ویژگی‌های موثرتر توسط شبکه می‌گردد. همچنین جهت بالابردن سرعت آموزش و کاهش محسوس حجم محاسبات، لایه‌های تمام متصل شبکه EZFNet از پیش‌آموزش‌دیده توسط شبکه یادگیری افراطی (ELM) جایگزین شد. در انتها نیز قابلیت تعمیم ELM با استفاده از بروزرسانی وزن‌ها و بایاس‌های لایه وروی ELM توسط الگوریتم بهینه‌سازی کواتی (COA) بهبود داده شده است. جهت بررسی عملکرد سیستم، از شش معیار ارزیابی، استفاده شده است، که مدل پیشنهادی ما در معیارهای دقت، صحت، حساسیت، تشخیص‌پذیری، F1-score و MCC به ترتیب مقادیر 97.97، 95.70، 97.15، 98.28، 96.42 و 93.98 درصدی را به دست آورده است. نتایج به دست آمده افزایش دقت، بهبود کارآیی، کاهش چشمگیر زمان محاسبات و سطح عملکردی بالای سیستم پیشنهادی را نسبت به روش‌های موجود نشان می‌دهد.

کلیدواژه‌ها

موضوعات

پردازش تصویر

عنوان مقاله [English]

Breast cancer diagnosis using improved ZFNet-ELM network with COA optimization algorithm

نویسنده [English]

Sara Khosravi

Department of Computer, Payame Noor University, Tehran, Iran

چکیده [English]

Nowadays, many researchers use systems based on deep learning and artificial intelligence to diagnose and classify many diseases in the field of medicine, such as cancer diagnosis. Early and accurate diagnosis of breast cancer based on mammography images can greatly help doctors in timely treatment of the disease. On the other hand, most of the deep networks used in breast cancer (BC) detection systems are not suitable for real-time applications due to the use of fully connected (FC) layers, time-consuming training method, and high calculation volume. Therefore, in this article, a new hybrid method based on deep learning and swarm intelligence called EZFNet-ELM-COA is proposed for real-time detection of breast cancer. In the proposed network (Enhanced ZFNet), in the convolutional layers of the ZFNet network, large size filters have been replaced by two series of smaller size filters, and then the outputs of the filters have been joined together. The use of this multi-scale technique makes the network extract more effective features. Also, in order to increase the speed of training and significantly reduce the volume of calculations, fully connected layers of the pre-trained EZFNet network were replaced by an Extreme Learning Machine (ELM). At the end, the generalization capability of ELM has been improved by updating the weights and biases of the ELM layer by the Coati Optimization Algorithm (COA). In order to check the performance of the system, six evaluation criteria have been used, and our proposed model achieved values of 97.97, 95.70, 97.15, 98.28, 96.42 and 93.98 percent for accuracy, precision, sensitivity, recognizability, F1-score and MCC, respectively. The obtained results show the increase of accuracy, improvement of efficiency, significant reduction of calculation time, and high performance level of the proposed system compared to the existing methods.

کلیدواژه‌ها [English]

Deep learning
Artificial intelligence
Breast cancer diagnosis
Deep convolutional neural network
Coati Optimization Algorithm (COA)

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