Classification of Alzheimer’s Disease via Eight-Layer Convolutional Neural Network with Batch Normalization and Dropout Techniques

More than 35 million patients are suffering from Alzheimer’s disease and this number is growing, which puts a heavy burden on countries around the world. Early detection is of benefit, in which the deep learning can aid AD identification effectively and gain ideal results. A novel eight-layer convolutional neural network with batch normalization and dropout techniques for classification of Alzheimer’s disease was proposed. After data augmentation, the training dataset contained 7399 AD patient and 7399 HC subjects. Our eight-layer CNN-BN-DO-DA method yielded a sensitivity of 97.77%, a specificity of 97.76%, a precision of 97.79%, an accuracy of 97.76%, a F1 of 97.76%, and a MCC of 95.56% on the test set, which achieved the best performance in seven state-of-the-art approaches. The results strongly demonstrate that this method can effectively assist the clinical diagnosis of Alzheimer’s disease.

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A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease

Brain Sciences ◽

10.3390/brainsci10020084 ◽

2020 ◽

Vol 10 (2) ◽

pp. 84 ◽

Cited By ~ 6

Author(s):

Atif Mehmood ◽

Muazzam Maqsood ◽

Muzaffar Bashir ◽

Yang Shuyuan

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Convolutional Neural Network ◽

State Of The Art ◽

Early Stage ◽

The State ◽

Test Accuracy ◽

Learning Approaches

Alzheimer’s disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer’s disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.

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Automated Classification of Alzheimer’s Disease Based on MRI Image Processing using Convolutional Neural Network (CNN) with AlexNet Architecture

Journal of Physics Conference Series ◽

10.1088/1742-6596/1844/1/012020 ◽

2021 ◽

Vol 1844 (1) ◽

pp. 012020

Author(s):

Y N Fu’adah ◽

I Wijayanto ◽

N K C Pratiwi ◽

F F Taliningsih ◽

S Rizal ◽

...

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Image Processing ◽

Alzheimer's Disease ◽

Convolutional Neural Network ◽

Automated Classification ◽

Mri Image

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Tomato pest classification using deep convolutional neural network with transfer learning, fine tuning and scratch learning

Intelligent Decision Technologies ◽

10.3233/idt-200192 ◽

2021 ◽

pp. 1-10

Author(s):

Gayatri Pattnaik ◽

Vimal K. Shrivastava ◽

K. Parvathi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Data Augmentation ◽

State Of The Art ◽

Deep Convolutional Neural Network ◽

Fine Tuning ◽

Tomato Plants ◽

Random Weights

Pests are major threat to economic growth of a country. Application of pesticide is the easiest way to control the pest infection. However, excessive utilization of pesticide is hazardous to environment. The recent advances in deep learning have paved the way for early detection and improved classification of pest in tomato plants which will benefit the farmers. This paper presents a comprehensive analysis of 11 state-of-the-art deep convolutional neural network (CNN) models with three configurations: transfers learning, fine-tuning and scratch learning. The training in transfer learning and fine tuning initiates from pre-trained weights whereas random weights are used in case of scratch learning. In addition, the concept of data augmentation has been explored to improve the performance. Our dataset consists of 859 tomato pest images from 10 categories. The results demonstrate that the highest classification accuracy of 94.87% has been achieved in the transfer learning approach by DenseNet201 model with data augmentation.

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