علوم مهارتی و خلاقیت

علوم مهارتی و خلاقیت

تشخیص زودهنگام سرطان سینه با تحلیل LSD و طبقه‌بند KNN بر روی بانک داده ماموگرافیِ MIAS

نوع مقاله : مقاله پژوهشی

نویسندگان
1 استادیار، گروه مهندسی کامپیوتر، دانشگاه فنی و حرفه‌ای، تهران، ایران.
2 مربی، گروه مهندسی کامپیوتر، دانشگاه فنی و حرفه‌ای، تهران، ایران.
چکیده
نظریۀ سرطان سینه، رایج‌ترین سرطان در میان زنان به‌خصوص در بین زنان بالای 50 سال است. مطالعات اخیر ثابت کرده‌اند که‌ اگر سرطان سینه در مراحل اولیه‌ تشکیلِ بافت‌های سرطانی تشخیص داده شود، احتمال حیات به‌طور قابل‌توجهی افزایش یافته و هزینه‌های ناشی از کنترل بیماری به شدت کاهش ‌یافته است بنابراین راه‌حل اصلی، شناسایی زودهنگام سرطان سینه است. تاکنون پژوهش‌های مختلفی برای تشخیص سرطان سینه ارائه شده است اما به دلیل انتخاب ویژگی‌های غیرمؤثر و همچنین استفاده‌نکردن از یک روش تحلیلی مناسب بر روی ویژگی‌ها نتوانستند به دقت کافی برسند. در این مطالعه از تحلیل LSD و استخراج ویژگی‌های مؤثر توسط طبقه‌بند KNN برای تشخیص خودکار سرطان سینه استفاده شده است. هدف از ارائه روش پیشنهادی، افزایش دقت تشخیص برای کلاس‌های نرمال و غیرنرمال می‌باشد. روش پیشنهادی در محیط متلب و بر روی بانک تصاویر ماموگرافیِ MIAS اجرا شده است. نتایج حاصل از خروجی پیاده‌سازی، بیانگر شناسایی سرطان سینه با دقت 92 درصد است. نتایج به‌دست‌آمده با سایر روش‌ها مقایسه شدند که نشان‌دهنده‌ عملکرد بهتر روش پیشنهادی از نظر معیار دقت می‌باشد.  
کلیدواژه‌ها

عنوان مقاله English

Early Detection of Breast Cancer by LSD Analysis and KNN Classification on MIAS Mammography Database

نویسندگان English

Farnaz Hoseini 1
Hamed SepehrZadeh 1
Masume Kheyri 2
1 Assistant Professor, Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.
2 The Coach, Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.
چکیده English

Breast cancer is the most common cancer among women, particularly among women over 50 years old. Recent studies have proven that if breast cancer is diagnosed in the early stages of the formation of cancerous tissues, the chance of survival increases significantly and the costs of controlling the disease are greatly reduced. Therefore, the main solution is early detection of breast cancer. Until now, various research has been presented to diagnose breast cancer, but due to the selection of ineffective features and also the lack of using a suitable analytical method on the features, they could not achieve sufficient accuracy. In this study, LSD analysis and extraction of effective features by KNN classifier are used for automatic detection of breast cancer. The purpose of presenting the proposed method is to increase the accuracy of diagnosis for normal and non-normal classes. The proposed method was implemented in MATLAB environment and on the MIAS mammography image bank. The results obtained from the implementation output demonstrated the detection of breast cancer with 92% accuracy. The obtained results were compared with other methods, which shows the better performance of the proposed method in terms of accuracy criteria.

کلیدواژه‌ها English

Breast Cancer
Mammography Images
Gabor Wavelet
LSD Analysis
Feature Extraction
MIAS Database
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  • تاریخ دریافت 25 مهر 1402
  • تاریخ بازنگری 20 آذر 1402
  • تاریخ پذیرش 23 بهمن 1402