Towards Practical Application of Deep Learning in Diagnosis of Alzheimer's Disease

• URL: http://arxiv.org/abs/2212.04528v1
• Date: Thu, 8 Dec 2022 19:21:51 GMT
• Title: Towards Practical Application of Deep Learning in Diagnosis of Alzheimer's Disease
• Authors: Harshit Parmar and Eric Walden
• Abstract summary: 3D version of well-known 2D CNNs were designed, trained and tested for diagnosis of various stages of Alzheimer's disease. Deep learning approach shows good performance in differentiating various stages of AD for more than 1500 full brain volumes.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurate diagnosis of Alzheimer's disease (AD) is both challenging and time consuming. With a systematic approach for early detection and diagnosis of AD, steps can be taken towards the treatment and prevention of the disease. This study explores the practical application of deep learning models for diagnosis of AD. Due to computational complexity, large training times and limited availability of labelled dataset, a 3D full brain CNN (convolutional neural network) is not commonly used, and researchers often prefer 2D CNN variants. In this study, full brain 3D version of well-known 2D CNNs were designed, trained and tested for diagnosis of various stages of AD. Deep learning approach shows good performance in differentiating various stages of AD for more than 1500 full brain volumes. Along with classification, the deep learning model is capable of extracting features which are key in differentiating the various categories. The extracted features align with meaningful anatomical landmarks, that are currently considered important in identification of AD by experts. An ensemble of all the algorithm was also tested and the performance of the ensemble algorithm was superior to any individual algorithm, further improving diagnosis ability. The 3D versions of the trained CNNs and their ensemble have the potential to be incorporated in software packages that can be used by physicians/radiologists to assist them in better diagnosis of AD.

Related papers

• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease [2.6793044027881865]
  This study proposes a novel Alzheimer's disease detection model based on Convolutional Neural Networks. The experimental results indicate that the employed 2D fusion algorithm effectively improves the model's training expense.
  arXiv  Detail & Related papers  (2024-01-25T12:18:46Z)
• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• 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)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia [0.0]
  Alzheimer's disease and Frontotemporal dementia are common forms of neurodegenerative dementia. Current structural imaging methods mainly focus on the detection of each disease but rarely on their differential diagnosis. We propose a deep learning based approach for both problems of disease detection and differential diagnosis.
  arXiv  Detail & Related papers  (2022-11-25T13:25:18Z)
• Towards better Interpretable and Generalizable AD detection using Collective Artificial Intelligence [0.0]
  Deep learning methods have been proposed to automate diagnosis and prognosis of Alzheimer's disease. These methods often suffer from a lack of interpretability and generalization. We propose a novel deep framework designed to overcome these limitations.
  arXiv  Detail & Related papers  (2022-06-07T13:02:53Z)
• Self Context and Shape Prior for Sensorless Freehand 3D Ultrasound Reconstruction [61.62191904755521]
  3D freehand US reconstruction is promising in addressing the problem by providing broad range and freeform scan. Existing deep learning based methods only focus on the basic cases of skill sequences. We propose a novel approach to sensorless freehand 3D US reconstruction considering the complex skill sequences.
  arXiv  Detail & Related papers  (2021-07-31T16:06:50Z)
• Input Agnostic Deep Learning for Alzheimer's Disease Classification Using Multimodal MRI Images [1.4848525762485871]
  Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes.
  arXiv  Detail & Related papers  (2021-07-19T08:19:34Z)
• 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)

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.