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فصلنامه علمی کارافن

ارائه مدل هوشمند تجمیعی مدیریت عدم قطعیت در شبکه باور عمیق برای طبقه بندی تصاویر میکروسکوپی به منظور تشخیص سرطان رحم

نوع مقاله : مقاله پژوهشی (کاربردی)

نویسندگان
1 دانشجوی دکتری هوش مصنوعی و رباتیکز دانشکده مهندسی کامپیوتر دانشگاه آزاد شهر قدس، تهران، ایران.
2 دانشیار دانشکده مهندسی کامپیوتر دانشگاه آزاد شهر قدس، تهران، ایران.
چکیده
تجزیه و تحلیل تصاویر پزشکی به منظور تشخیص انواع سرطان در سالهای اخیر موردتوجه پژوهشگران قرار گرفته است. پژوهش حاضر یک مدل تجمیعی، با استفاده از قابلیت تئوری باور و یادگیری عمیق به منظور پردازش تصاویر پاپ اسمیر، برای تشخیص سرطان دهانه رحم ارائه می کند. در مدل طبقه بندی تجمیعی، از ترکیب مدل های طبقه بندی و شبکه باور بهره گرفته شده است. در این روش، از شبکه باور و قانون ترکیب دمپستر جهت ترکیب مشاهدات برای مدیریت عدم قطعیت طبقه بندی انواع سلولی استفاده می شود. این مدل به منظور مدیریت عدم قطعیت در نمونه های مشابه طبقه بندی نشده مدل دمپستراز مدل طبقهبندی تجمیعی پیشنهادی بهره می برد. این روش پیشنهادی، از پنج طبقه بند شامل ماشین بردار پشتیبان، نزدیکترین همسایه کی، درخت تصمیم، نایو بیزین و طبقه بند فازی برای طبقه بندی نمونه ها و بهبود عملکرد مدل استفاده می کند. نتایج پیادهسازی مدل پیشنهادی برروی دو مجموعه داده استاندارد مورد بررسی قرار گرفت. این مدل در مجموعه داده Sipakmed در معیار دقت با /۹۳ ۹۸ درصد، نسبت به روش های موجود برتری داشته است و در مجموعه داده Herlev با حساسیت /۸۳ ۹۸ د رصد، و سطح زیر نمودار ROC /۹۴ ۹۹ د رصد، نسبت به روش های موجود برتری داشته است که برای تشخیص زودهنگام این نوع از سرطان امید بخش است.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

An Intelligent Ensemble Model of Uncertainty Management in Belief Network for the Classification of Microscopic Images to Detect Cervical Cancer

نویسندگان English

Mona Benhari 1
Rahil Hosseini 2
1 PhD Candidate, Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.
2 Associate Professor Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran.
چکیده English

The analysis of medical images in recent years in order to diagnose types of cancer has attracted the attention of researchers. The present research presents a comprehensive model, using the ability of belief theory and deep learning to process pap smear images, for the diagnosis of cervical cancer. In the aggregate classification model, the combination of classification models and belief network is used. In this method, it uses the belief network and Dempster's combination law to combine observations to manage the uncertainty of cell types classification. This model uses the proposed cumulative classification model in order to manage uncertainty in similar unclassified samples. This proposed method uses five classifiers including support vector machine, K-nearest neighbor, decision tree, Naive Bayesian and fuzzy classifier to classify samples and improve model performance. The results of the implementation of the proposed model were analyzed on two standard data sets. In the Sipakmed data set, this model was superior to the existing methods in terms of accuracy with 98.93%, and in the Herlev data set, it was superior to the existing methods with a sensitivity of 98.83%, and the level under the ROC chart was 99.94%. which is promising for early detection of this type of cancer. 

کلیدواژه‌ها English

Convolutional Neural Network Modeling Uncertainty Dempster
shafer Theory Belief Network Cell Image Classification
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دوره 21، شماره 1 - شماره پیاپی 66
فنی و مهندسی
بهار 1403
صفحه 89-69

  • تاریخ دریافت 20 تیر 1402
  • تاریخ بازنگری 08 اسفند 1402
  • تاریخ پذیرش 17 فروردین 1403