Deep Learning-Based Alzheimer Disease Detection
Deep learning methods have gained more popularity recently in medical image analysis. This work proposes a deep convolutional neural network (DCNN) for Alzheimer’s disease classification using magnetic resonance imaging (MRI) samples. Alzheimer disease (A
- PDF / 471,893 Bytes
- 11 Pages / 439.37 x 666.142 pts Page_size
- 48 Downloads / 232 Views
Abstract Deep learning methods have gained more popularity recently in medical image analysis. This work proposes a deep convolutional neural network (DCNN) for Alzheimer’s disease classification using magnetic resonance imaging (MRI) samples. Alzheimer disease (AD) is an irreversible neurological brain disorder; its early symptoms are memory loss and losing thinking abilities called cognitive functions. The accurate diagnosis of Alzheimer’s disease at an early stage is very vital for patient care and conducting future treatment. Deep learning techniques are capable of learning high-level features from dataset compared to hand-crafted feature learning methods such as machine learning techniques. The proposed method classifies the disease as Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal control (NC). Spyder software obtained from anaconda bundle with Keras library and Tensorflow backend on GPU is used to model DCNN. Experiments are conducted using ADNI dataset and output classification result showed 98.57% accuracy compared to other studies. Our approach also enables us to expand this methodology to predict for more stages of disease classification. Keywords Alzheimer’s disease · Convolutional neural network · Deep learning · MRI · Neurological disorder
1 Introduction Alzheimer’s disease (AD) is a progressive neurological brain disease, which is caused due to the damage of nerve cells in parts of the brain [1]. It begins with the loss of memory, difficulty in speaking language and other cognitive functions making a S. S. Kundaram (B) · K. C. Pathak Department of Electronics and Communication, Sarvajanik College of Engineering and Technology, Gujarat Technological University, Surat, India e-mail: [email protected] K. C. Pathak e-mail: [email protected] © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 V. Nath and J. K. Mandal (eds.), Proceedings of the Fourth International Conference on Microelectronics, Computing and Communication Systems, Lecture Notes in Electrical Engineering 673, https://doi.org/10.1007/978-981-15-5546-6_50
587
588
S. S. Kundaram and K. C. Pathak
Fig. 1 Proposed deep learning flow for classification of Alzheimer’s into AD, MCI and NC
patient unable to perform day-to-day life activities. In particular, [1] researchers found that AD is not only common cause of dementia but eventually leading to death of people, which become a remarkable focus in research (Fig. 1). According to Alzheimer’s association, it is the sixth leading cause of death in the USA[2]. A survey [3] stated that there will 131.5 million people living with dementia worldwide and most of them with age greater than 65 has higher rate of risk with this disease. The brain region including thinking ability, memory, reasoning of the patient wrinkle up and shrinks in the hippocampus area. This is the main cause of suffering from AD. The visualization of AD and healthy brain shown in Fig. 2 gives the idea that memory and language m
Data Loading...
