ارائه‌ مدل‌هاي محاسبات نرم مبتني بر فازي، تكاملي و هوش جمعي در تحليل تصاوير ماموگرافي جهت تشخيص تومور‌هاي سينه

Soft Computing Methods based on Fuzzy, Evolutionary and Swarm Intelligence for Analysis of Digital Mammography Images for Diagnosis of Breast Tumors

مدل‌هاي محاسبات نرم مبتني بر سامانه‌هاي هوشمند فازي درتشخيص سرطان سينه، امكان مديريت عدم قطعيت در فرايند استدلال در سامانه‌ را فراهم مي‌كند.در اين پژوهش، يك مدل استنتاج فازي به‌منظور مديريت عدم قطعيت در داده‌هاي ورودي طراحي شده است و الگوريتم‌هاي هايبريدي مبتني بر فازي جهت تنظيم و بهينه‌سازي پارامتر‌ها، به كار برده شده‌اند. هدف، ارائه روش‌هاي مؤثر جهت تشخيص نوع توده‌هاي خوش‌خيم، بدخيم و نرمال سينه است. طبقه‌بندي توده‌ها جهت تشخيص موارد نرمال، خوش‌خيم و بدخيم با مدل‌هاي هايبريدي محاسبات نرم و بر اساس تحليل ويژگي‌ها در تصاوير ماموگرافي انجام شده است. الگوريتم‌هاي هايبريدي ارايه‌شده در اين پژوهش شامل1 ) فازي- ژنتيك، 2) فازي- بهينه‌سازي ازدحام ذرات و 3) فازي- بهينه‌سازي مبتني بر جغرافياي زيستي است. به‌منظور سنجش عملكرد سامانه از تحليل منحني مشخصه(ROC)و همچنين از روش اعتبار‌سنجي تقاطعي ده‌بخشي جهت تقسيم‌بندي داده‌ها به بخش‌هاي آموزش و آزمون براي به‌دست‌آوردن نتايج قابل اعتماد و اعتبار‌سنجي استفاده شده است. نوآوري پژوهش حاضر در ارايه مدل پيشنهادي هايبريدي فازي- بهينه‌سازي مبتني بر جغرافياي زيستي و بهبود عملكرد مدل طبقه‌بندي جهت تشخيص سرطان سينه است. روش‌ جديد هايبريدي فازي- بهينه‌سازي مبتني بر جغرافياي زيستي ارايه‌شده به‌منظور تشخيص سرطان سينه در اين پژوهش، عملكرد بالاتري نسبت به روش‌هاي موجود بر روي اين بانك اطلاعاتي معتبر و معروف جهت تشخيص سرطان سينه داشته است. باتوجه به نتايج به‌دست آمده و مقايسه عملكرد مدل‌هاي هايبريدي پيشنهادي در اين پژوهش، روش ‌هايبريدي فازي مبتني بر جغرافياي زيستي با ميزان صحت 95/25% از عملكرد بهينه‌تري نسبت به روش‌هاي هايبريدي پيشنهادي ديگر جهت تشخيص سرطان سينه برخوردار است. مدل حاضر در مقايسه با ساير مدل‌هاي پيشنهادي در پژوهش‌هاي قبلي بهبود يافته است. استفاده از مدل‌هاي پيشنهادي در اين پژوهش، مي‌تواند به‌منظور تشخيص زود‌هنگام بيماري و ارائه درمان‌هاي مؤثر اميد‌بخش باشد.

Soft computing models based on intelligent fuzzy systems have the capability of managing uncertainty in the image based practices of disease. Analysis of the breast tumors and their classification is critical for early diagnosis of breast cancer as a common cancer with a high mortality rate between women all around the world. Soft computing models based on fuzzy and evolutionary algorithms play an important role in advances obtained in computer aided detection (CAD) systems. Combination of the evolutionary nature of swarm intelligence algorithms in optimization along with the potential of fuzzy models to cope with uncertainty and complex environments. In this research, a fuzzy inference model has been proposed for managing uncertainty in input data. The main uncertainty issues for classification of the breast tumors were modeled through the linguistic terms, fuzzy variables and fuzzy reasoning processes in the fuzzy inference model. Fuzzy linguist terms and rule sets are valuable to have an intelligent model with the ability to interact with the clinicians. Furthermore, hybrid fuzzy-evolutionary models have been proposed for tuning fuzzy membership functions for diagnosis of malignant and benign breast tumors. The hybrid proposed evolutionary methods are: 1) Fuzzy-Genetic, 2) Fuzzy-Particle swarm intelligence, and 3) Fuzzy-biogeography models. Evolutionary nature inspired combination with the fuzzy inference model (FIM) improves the proficiency of the FIM by adaption to the environment through the tuning process using training and testing datasets. To achieve this, the Genetic Algorithm was applied as a base evolutionary method. Then, the potential of the Particle Swart intelligence algorithm in using local and global experiences of the solutions in the search space. Also, bio-geographical aspects of species in finding an optimum solution lands with the high suitability habitat index has been concentrated in optimization process of the FIM. Evolutionary algorithms perform tuning of the fuzzy membership functions to improve the accuracy of the fuzzy inference model while simplicity and interpretability of the FIM was kept. For performance evaluation, an ROC curve analysis was conducted which is a robust and reliable technique that represents the trades of between classification model benefits and costs. Also, for validation purpose, a 10-fold cross-validation technique was performed for partitioning the dataset into training and testing sets in the evolutionary optimization algorithms. The performance of the proposed methods were evaluated using a dataset including 295 images and extracted features from mammographic image analysis society (MIAS) dataset. The results reveal that the hybrid Fuzzy-biogeography model outperforms the other evolutionary models with an accuracy and area under the ROC curve (AUC) of 95.25%, and 91.43%, respectively. Performance comparison of the hybrid evolutionary models in this study with the related methods for classification of the breast tumors on the MIAS dataset reveals that the fuzzy-biogeography model outperforms the other methods in terms of trades-off between accuracy and interpretability with an area under the ROC curve of 95.25% with four extracted features. The Fuzzy-GA and Fuzzy-Swarm Intelligence models are competitive with the best results of counterpart methods with an accuracy of 93.9% and 94.58% in terms of the AUC, respectively. The proposed fuzzy-evolutionary models in this study are promising for diagnosis of the breast tumors in early stages of the disease and providing suitable treatment.