Worldwide interest has been noted in medical image analysis and classification using machine learning techniques. Magnetic resonance imaging (MRI) is one of the safe and painless procedures for human brain scanning. During the MRI procedure, magnetic fields and radio waves are used to scan and map the extended view of brain tissues for further pathological processes and analysis. For a qualitative and quantitative MRI analysis, the manual capability of radiologists and/or doctors is limited and time-consuming in complex and group-level diagnoses. Hence, the development of an intelligent, robust, and reliable support system for the diagnosis of brain-related diseases is a top priority. In this paper, a new deep neural network based MRI image classification approach is proposed that uses fuzzy c-mean (FCM) and an autoencoder to classify brain MRI as normal or abnormal, diminishing human error during the diagnosis of diseases in MRI scans. Here, FCM is utilized for abnormal tissue segmentation from brain MRI images, followed by an autoencoder, for extraction and dimensionality reduction of features. Finally, a deep neural network was used for the classification of brain MRI images that were trained using FCM-extracted features and sample data. Considering the availability of raw MRI data, data augmentation techniques have also been used to increase the number of data required to train a deep neural network. The experiment results achieved 96% accuracy and a 95% sensitivity rate for classification. The results demonstrate that the proposed well-trained deep learning technology has the potential to make solid predictions regarding brain abnormalities; therefore, it can be used as a prominent tool in clinical practice.

Keywords: Magnetic resonance imaging (MRI), Fuzzy c-mean (FCM) clustering, Deep neural network, Autoencoder, Classification

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