VDRNet19: a dense residual deep learning model using stochastic gradient descent with momentum optimizer based on VGG-structure for classifying dementia

Dementia disease is a syndrome caused by various disorders and conditions that affect the brain which causes gradual decline in neurological function commonly observed in older individuals. The disease is categorized into three stages in our research: Mild dementia (MD), Non-dementia (ND) and very mild dementia (VMD). Magnetic Resonance Imaging (MRI) scan of the brain is used for diagnosing dementia. In this research, a dense residual deep learning model using stochastic gradient descent with momentum optimizer based on VGG-structure for classifying dementia (VDRNet19) is proposed, which can detect all three stages of dementia The proposed model is trained and tested with the Open Access Series of Imaging and Studies (OASIS) dataset. In this work, the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement method is employed to preprocess the raw for analysis. In order to confront the imbalance in dataset, augmentation techniques are used. As a result, a balanced dataset comprising a total of 1941 images across the three classes are obtained. Initially, six existing models including DenseNet201, VGG19, ResNet152, AlzheimerNet [13], MobileNetV2 and ensemble of pretrained networks were trained and tested to attain 93.84%, 92.42%, 91.1%, 89.73%, 87.67% and 94.86% of test accuracies respectively. DenseNet201, VGG19, ResNet152 yields the highest accuracy, which is the backbone to design the proposed model. VDRNet19 using optimizer as stochastic gradient descent with momentum, 0.01 as learning rate, achieves the highest testing accuracy of 97.26%. This study compared six pre-trained models alongside the proposed model in terms of performance metrics to determine if the VDRNet19 model excels in classifying the three classes. An ablation study was conducted to validate the chosen hyperparameters. Results indicate that the proposed model surpasses traditional methods in classifying dementia stages from brain MRI scan images.