Affiliations: [a] Department of Computer Science and Engineering, The NorthCap University, Gurugram, India | [b] Delhi Skill and Entrepreneurship University, India | [c] Department of Neurosurgery, Fortis Hospital, New Delhi, India | [d] Department of NeuroSurgery, Max Super Speciality Hospital, New Delhi, India
Correspondence:
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Corresponding author: Deepika Bansal, Department of Computer Science and Engineering, The NorthCap University, Gurugram, India. E-mail: dbansal7@yahoo.com.
Abstract: Automated models using deep learning are more extensively used in medical imaging in the last decade. The present study contributes to the diagnosis of dementia using MRI. Dementia is a syndrome that deteriorates the cognitive function of the brain. The disease has no cure, till now, except for the prior diagnosis. The present study aims for classifying the MRI scans of two datasets OASIS and ADNI into 2 categories: binary and multi-classification. To achieve the objective, the EfficientNetB0 architecture of deep learning is fine-tuned by adding three dense layers on the top of the network. The swish activation function is used in the inner dense layers added. The dropout and batch normalization layers are also added for dealing with the problem of overfitting. This architecture offers high accuracy and high efficiency compared to other pre-trained networks. The model is assessed on various performance measures and outperformed the state of art techniques. For the OASIS dataset, the best testing accuracy for binary classification is 93.10% with a 0.01 learning rate. The sensitivity is 95.93%, specificity is 90.08%, false-negative rate is 4.07, the false-positive rate is 9.92 and the F1-score is 93.48%. The best testing accuracy of multi-classification is 84.50% with a 0.001 learning rate. For the ADNI dataset, the best testing accuracy is 96.08% with a learning rate of 0.001. The sensitivity is 94.74%, specificity is 99.32%, false-negative rate is 5.26, the false-positive rate is 0.68 and the f1-score is 97.16%. The best testing accuracy of multi-classification is 98.10 with a 0.01 learning rate. The proposed model can be utilized for developing an automated framework to help medical services to improve decision-making.
Keywords: Dementia, magnetic resonance imaging, pre-trained networks, transfer learning, efficientnet
