SNeurodCNN: Structure-focused Neurodegeneration Convolutional Neural Network for Modelling and Classification of Alzheimer's Disease
- URL: http://arxiv.org/abs/2401.03922v3
- Date: Fri, 31 May 2024 01:10:42 GMT
- Title: SNeurodCNN: Structure-focused Neurodegeneration Convolutional Neural Network for Modelling and Classification of Alzheimer's Disease
- Authors: Simisola Odimayo, Chollette C. Olisah, Khadija Mohammed,
- Abstract summary: Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge.
Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error.
This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosis. Current clinical diagnoses rely on radiologist expert interpretation, which is prone to human error. Deep learning has thus far shown promise for early AD diagnosis. However, existing methods often overlook focal structural atrophy critical for enhanced understanding of the cerebral cortex neurodegeneration. This paper proposes a deep learning framework that includes a novel structure-focused neurodegeneration CNN architecture named SNeurodCNN and an image brightness enhancement preprocessor using gamma correction. The SNeurodCNN architecture takes as input the focal structural atrophy features resulting from segmentation of brain structures captured through magnetic resonance imaging (MRI). As a result, the architecture considers only necessary CNN components, which comprises of two downsampling convolutional blocks and two fully connected layers, for achieving the desired classification task, and utilises regularisation techniques to regularise learnable parameters. Leveraging mid-sagittal and para-sagittal brain image viewpoints from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, our framework demonstrated exceptional performance. The para-sagittal viewpoint achieved 97.8% accuracy, 97.0% specificity, and 98.5% sensitivity, while the mid-sagittal viewpoint offered deeper insights with 98.1% accuracy, 97.2% specificity, and 99.0% sensitivity. Model analysis revealed the ability of SNeurodCNN to capture the structural dynamics of mild cognitive impairment (MCI) and AD in the frontal lobe, occipital lobe, cerebellum, temporal, and parietal lobe, suggesting its potential as a brain structural change digi-biomarker for early AD diagnosis. This work can be reproduced using code we made available on GitHub.
Related papers
- A Dual-Attention Aware Deep Convolutional Neural Network for Early Alzheimer's Detection [0.0]
Alzheimer's disease (AD) represents the primary form of neurodegeneration, impacting millions of individuals each year.
We introduce a dual attention enhanced deep learning framework for classifying AD from neuroimaging data.
arXiv Detail & Related papers (2024-07-15T17:22:16Z) - UniBrain: Universal Brain MRI Diagnosis with Hierarchical
Knowledge-enhanced Pre-training [66.16134293168535]
We propose a hierarchical knowledge-enhanced pre-training framework for the universal brain MRI diagnosis, termed as UniBrain.
Specifically, UniBrain leverages a large-scale dataset of 24,770 imaging-report pairs from routine diagnostics.
arXiv Detail & Related papers (2023-09-13T09:22:49Z) - Attention-based 3D CNN with Multi-layer Features for Alzheimer's Disease
Diagnosis using Brain Images [21.514626584695897]
We propose an end-to-end 3D CNN framework for Alzheimer's disease diagnosis based on ResNet.
Our model can focus on key brain regions related to the disease diagnosis.
Our method was verified in ablation experiments with two modality images on 792 subjects.
arXiv Detail & Related papers (2023-08-10T15:53:35Z) - NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical
Development Patterns of Preterm Infants [73.85768093666582]
We propose an explainable geometric deep network dubbed NeuroExplainer.
NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
arXiv Detail & Related papers (2023-01-01T12:48:12Z) - Hierarchical Graph Convolutional Network Built by Multiscale Atlases for
Brain Disorder Diagnosis Using Functional Connectivity [48.75665245214903]
We propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis.
We first use a set of well-defined multiscale atlases to compute multiscale FCNs.
Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling.
arXiv Detail & Related papers (2022-09-22T04:17:57Z) - Wide & Deep neural network model for patch aggregation in CNN-based
prostate cancer detection systems [51.19354417900591]
Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020.
To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images.
Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
arXiv Detail & Related papers (2021-05-20T18:13:58Z) - Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease
Using Structural and Synthesized Functional MRI Data [8.388888908045406]
We propose a potential solution by first learning a structural-to-functional transformation in brain MRI.
We then synthesize spatially matched functional images from large-scale structural scans.
We identify the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model.
arXiv Detail & Related papers (2021-04-10T03:16:33Z) - Deep Convolutional Neural Network based Classification of Alzheimer's
Disease using MRI data [8.609787905151563]
Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory.
In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset.
The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control: and has achieved 99.89% classification accuracy with imbalanced classes.
arXiv Detail & Related papers (2021-01-08T06:51:08Z) - Towards a quantitative assessment of neurodegeneration in Alzheimer's
disease [0.0]
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that progressively destroys memory and other cognitive domains of the brain.
This paper introduces the notion of a pathology specific imaging contrast (PSIC), which can serve as a means of visual representation of the spatial extent of neurodegeneration.
The values of PSIC are computed by a dedicated deep neural network (DNN), which has been specially adapted to the processing of dMRI signals.
arXiv Detail & Related papers (2020-11-06T05:56:29Z) - A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease
Progression with MEG Brain Networks [59.15734147867412]
Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression.
We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G)
We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
arXiv Detail & Related papers (2020-05-08T02:29:24Z) - Retinopathy of Prematurity Stage Diagnosis Using Object Segmentation and
Convolutional Neural Networks [68.96150598294072]
Retinopathy of Prematurity (ROP) is an eye disorder primarily affecting premature infants with lower weights.
It causes proliferation of vessels in the retina and could result in vision loss and, eventually, retinal detachment, leading to blindness.
In recent years, there has been a significant effort to automate the diagnosis using deep learning.
This paper builds upon the success of previous models and develops a novel architecture, which combines object segmentation and convolutional neural networks (CNN)
Our proposed system first trains an object segmentation model to identify the demarcation line at a pixel level and adds the resulting mask as an additional "color" channel in
arXiv Detail & Related papers (2020-04-03T14:07:41Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.