An expert system for brain tumor detection: Fuzzy C-means with super resolution and convolutional neural network with extreme learning machine

Super-resolution, which is one of the trend issues of recent times, increases the resolution of the images to higher levels. Increasing the resolution of a vital image in terms of the information it contains such as brain magnetic resonance image (MRI), makes the important information in the MRI image more visible and clearer. Thus, it is provided that the borders of the tumors in the related image are found more successfully. In this study, brain tumor detection based on fuzzy C-means with super-resolution and convolutional neural networks with extreme learning machine algorithms (SR-FCM-CNN) approach has been proposed. The aim of this study has been segmented the tumors in high performance by using Super Resolution Fuzzy-C-Means (SR-FCM) approach for tumor detection from brain MR images. Afterward, feature extraction and pretrained SqueezeNet architecture from convolutional neural network (CNN) architectures and classification process with extreme learning machine (ELM) were performed. In the experimental studies, it has been determined that brain tumors have been better segmented and removed using SR-FCM method. Using the SquezeeNet architecture, features were extracted from a smaller neural network model with fewer parameters. In the proposed method, 98.33% accuracy rate has been detected in the diagnosis of segmented brain tumors using SR-FCM. This rate is greater 10% than the rate of recognition of brain tumors segmented with fuzzy C-means (FCM) without SR.