On Disharmony in Batch Normalization and Dropout Methods for Early Categorization of Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a global health issue that predominantly affects older people. It affects one’s daily activities by modifying neural networks in the brain. AD is categorized by the death of neurons, the creation of amyloid plaques, and the development of neurofibrillary tangles. In clinical settings, an early diagnosis of AD is critical to limit the problems associated with it and can be accomplished using neuroimaging modalities, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Deep learning (DL) techniques are widely used in computer vision and related disciplines for various tasks such as classification, segmentation, detection, etc. CNN is a sort of DL architecture, which is normally useful to categorize and extract data in the spatial and frequency domains for image-based applications. Batch normalization and dropout are commonly deployed elements of modern CNN architectures. Due to the internal covariance shift between batch normalization and dropout, the models perform sub-optimally under diverse scenarios. This study looks at the influence of disharmony between batch normalization and dropout techniques on the early diagnosis of AD. We looked at three different scenarios: (1) no dropout but batch normalization, (2) a single dropout layer in the network right before the softmax layer, and (3) a convolutional layer between a dropout layer and a batch normalization layer. We investigated three binaries: mild cognitive impairment (MCI) vs. normal control (NC), AD vs. NC, AD vs. MCI, one multiclass AD vs. NC vs. MCI classification problem using PET modality, as well as one binary AD vs. NC classification problem using MRI modality. In comparison to using a large value of dropout, our findings suggest that using little or none at all leads to better-performing designs.

Automated Classification of Brain Tumors from Magnetic Resonance Imaging Using Deep Learning

“…In the same way, we added the number of convolution layers with the filter size of 32, 64, 128, and 256 having the same kernel size of 3 × 3, stride size of 1 × 1, and padding is valid. Subsequently, we applied the global average pooling [ 49 ], flattened, dense [ 50 ] (in the dense layer, we used 512 neurons and kernel regularizing techniques L1 (10 −5 ) and L2 (10 −4 ), and dropout [ 51 ] layers with 0.5%. In the end, the softmax function [ 47 ] was utilized with the output layer to determine the likelihood score for each class and classify the decision label as to whether the input image contained a glioma, meningioma, or pituitary tumor.…”

Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques

“…To mitigate the issue of overfitting, the dense layer was subjected to regulation using L1 (10 −5 ) and L2 (10 −4 ) regularization techniques [ 53 ]. During the training process, the neurons within a dropout layer [ 54 ] were randomly deactivated at a rate of 0.5% to enhance regularization implementation further. Finally, the output layer employed the softmax algorithm [ 51 ] to compute the probability score for each class and classify whether the input image exhibited a glioma, meningioma, pituitary, or no tumor.…”

Analysis of Enrollment Criteria in Secondary Schools Using Machine Learning and Data Mining Approach

“…It can also be represented using a Bayesian network which is very basic. Naive Bayes classifiers are particularly popular for classification and are a traditional solution to problems such as spam detection [54][55][56]. NB performance, with truth data, classifier results, and accuracy, is presented in Figure 9.…”