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| تعداد شمارهها | 685 |
| تعداد مقالات | 9,958 |
| تعداد مشاهده مقاله | 70,839,462 |
| تعداد دریافت فایل اصل مقاله | 62,332,176 |
تولید داده های جدید ملانوما با استفاده از ترکیب شبکه متخاصم مولد و الگوی باینری محلی | ||
| مدل سازی در مهندسی | ||
| دوره 23، شماره 80، فروردین 1404، صفحه 147-158 اصل مقاله (714 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22075/jme.2024.31553.2515 | ||
| نویسندگان | ||
| ویدا اسماعیلی؛ محمود محصل فقهی* | ||
| دانشکده مهندسی برق و کامپیوتر، دانشگاه تبریز، تبریز، ایران | ||
| تاریخ دریافت: 29 مرداد 1402، تاریخ بازنگری: 25 فروردین 1403، تاریخ پذیرش: 24 تیر 1403 | ||
| چکیده | ||
| پایگاه دادههای درموسکوپیِ در دسترسِ سرطان پوست ملانوما دارای تصاویر کم و نامتوازن با روشنایی غیریکنواخت هستند که روشهای شناسایی ملانوما را با چالش مواجه کردهاند. برای حل این مشکلات، در این مقاله، روش جدیدی را برای تولید دادههای جدید شامل ملانوما پیشنهاد شده است. در واقع، روش جدیدِ پیشنهادی، شبکه متخاصم مولد را با الگوی باینری محلی ترکیب میکند. به عبارت دیگر، ابتدا تصاویر موجود در پایگاه داده برای آموزش وارد شبکهی متخاصم مولد میشوند. سپس، تصاویر جدید فراوانی تولید میشود و در نهایت، روش الگوی باینری محلی به آنها اعمال میشود. بنابراین، تعداد دادههای جدید تولید شده زیاد و متعادل است و دادههای تولید شده، تغییرات روشنایی ندارند. همچنین، این دادهها ویژگیهای مفید و معناداری را نشان میدهند که تمایز بین ملانوما و خال را بیشتر میسازد. آزمایشهای انجام شده نشان داده است که روش پیشنهاد تأثیر خوبی در افزایش دقت شناسایی ملانوما دارد. طبق نتایج روش پیشنهادی عملکرد شبکه عصبی کانولوشنی را %7 افزایش داده است. | ||
| کلیدواژهها | ||
| الگوی باینری محلی؛ تولید دادههای جدید؛ سرطان پوست؛ شبکه متخاصم مولد؛ ملانوما | ||
| عنوان مقاله [English] | ||
| Generating New Melanoma Data Using a Combination of Generative Adversarial Network and Local Binary Pattern | ||
| نویسندگان [English] | ||
| Vida Esmaeili؛ Mahmood Mohassel Feghhi | ||
| Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran | ||
| چکیده [English] | ||
| The available melanoma skin cancer dermoscopy databases have low and unbalanced images with non-uniform illumination that make melanoma detection methods challenging. To address these problems, in this paper, we propose a new method to generate new data including melanoma. In fact, our new proposed method combines generative adversarial network and local binary pattern. On the other hand, first, the images existing in the dataset are fed to the generative adversarial network. Then, many new images are generated and finally, the local binary pattern is applied to them. Therefore, the number of the new generated data is large and balanced and the generated data does not have illumination changes. Also, these data show useful and meaningful features that increase the difference between melanoma and nevi. The experiments have shown that the proposed method has a good effect on increasing the accuracy of melanoma diagnosis. According to the results, the proposed method has increased the convolutional neural network's efficiency by 7%. | ||
| کلیدواژهها [English] | ||
| Local binary pattern, Generating new data, Skin cancer, Generative adversarial network, Melanoma | ||
| مراجع | ||
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آمار تعداد مشاهده مقاله: 401 تعداد دریافت فایل اصل مقاله: 261 |
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