Deep learning approaches for MRI image classifications

Abstract

Although there is no treatment for Alzheimer’s disease (AD), but an accurate early diagnosis is very important for both the patient and public care. Our proposed model consists of two main experiments traditional machine learning and deep learning approaches. These two studies were carried out using the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset. In our first experiment, we tested different handcrafted feature descriptors to diagnose Alzheimer’s disease. But the most powerful and efficient descriptor is Monogenic Binary Coding (MBC) that gives maximum accuracy of 90.14 percent. In the second experiment, we propose a novel deep convolutional neural network for the diagnosis of Alzheimer’s disease and its stages using magnetic resonance imaging (MRI) scans. In this research, we propose a novel 3-way classifier to discriminate patients having AD, mild cognitive impairment (MCI), and normal control (NC) using a pre-trained CNN architecture Inceptionv3. The proposed method work on proficient technique to use transfer learning to identify the images by fine-tuning a pre-trained CNN architecture, Inceptionv3. The performance of the proposed system is calculated over the ADNI dataset. The proposed model showed novel results by giving the best overall accuracy of 95.71% for multiclass classification problems. Our experimental results show that the performance of the deep learning approaches is comparatively higher than the traditional machine learning.