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

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.