Early Detection of Hemiplegia by Analyzing the Gait Characteristics and Walking Patterns Using Convolutional Neural Networks

2020 ◽  
pp. 39-51
Author(s):  
Sagar Patil ◽  
Akshay Shah ◽  
Shubham Dalvi ◽  
Jignesh Sisodia
Keyword(s):  
Neural Networks ◽  
Early Detection ◽  
Convolutional Neural Networks ◽  
Gait Characteristics

scholarly journals OP0064 AUTOMATIC SCORING OF ARTHRITIS DISEASE ACTIVITY ON ULTRASOUND IMAGES FROM RHEUMATOID ARTHRITIS PATIENTS WITH CASCADED CONVOLUTIONAL NEURAL NETWORKS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 42.2-43
Author(s):  
A. Christensen ◽  
S. A. Just ◽  
J. K. H. Andersen ◽  
T. R. Savarimuthu
Keyword(s):  
Neural Network ◽  
Rheumatoid Arthritis ◽  
Neural Networks ◽  
Early Detection ◽  
Disease Activity ◽  
Convolutional Neural Networks ◽  
Scoring System ◽  
Cascade Model ◽  
Ultrasound Images ◽  
Automatic Scoring

Background:Systematic Power or Color Doppler (CD) ultrasound (US) of joints can be used for early detection of Rheumatoid Arthritis (RA), predicting radiographic progression and early detection of disease flare in established RA [1, 2]. The international standard for performing RA US scanning and evaluation of disease activity is the OMERACT-ELUAR Synovitis Scoring (OESS) system [1, 3].To further mitigate the operator-dependency in scoring disease activity on CD US images in future trials and clinical practice, we proposed the use of convolutional neural networks (CNN) to automatically grade CD US images according to the OESS definitions. This study is a continuation of the findings in our previous work, where we developed a CNN for four-class CD US OESS scoring with a test accuracy of 75.0% [4].Objectives:Since our last contribution, we have further developed the architecture of this neural network and can here present a new idea applying a Cascaded Convolutional Neural Network design. We evaluate the generalizability of this method on unseen data, comparing the CNN with an expert rheumatologist.Methods:The images used for developing the algorithms were graded by a single expert rheumatologist according to the OESS system. The CNNs in the cascade were trained individually, after which they were combined to form the cascade model as shown in figure 1. The algorithms were evaluated on a separate test dataset, which came from the same distribution as the training dataset. The algorithms were compared to the gradings of an expert rheumatologist on a per-joint basis using a Kappa test, and on a per-patient basis using a Wilcoxon Signed Rank test.Figure 1.CNN-1 is the first CNN in the model and distinguishes between RA disease grade (DG) 0 and DG’s 1, 2 and 3. CNN-2 is the second CNN and distinguishes between DG 1 and DG’s 2 and 3. CNN-3 is the final CNN which distinguishes between DG’s 2 and 3.Results:With 1678 images available for training and 322 images for testing the model, the model achieved an overall 4-class accuracy of 83.9%. On a per-patient level, there was no significant difference between the classifications of the model and of a human expert (p=0.85).Conclusion:We have shown that dividing a four-degree classification task into three successive binary classification tasks has resulted in a model capable of making correct classifications in 83.9% of the cases for a test set of ultrasound images with a naturally occurring distribution of RA joint disease activity scores.Furthermore, we have shown that the cascade model can produce classification decisions comparable with a human rheumatologist when applied on a per-patient basis. This emphasizes the potential of using CNNs with this architecture as a strong assistive tool for the objective assessment of disease activity of RA patients.References:[1]D’Agostino M-A, Terslev L, Aegerter P, et al. (2017). Scoring ultrasound synovitis in rheumatoid arthritis: a OMERACT-EULAR ultrasound taskforce-Part 1: definition and development of a standardised, consensus-based scoring system. RMD Open.[2]Paulshus NS, Aga A-B, Olsen I, et al. (2018). Clinical and ultrasound remission after 6 months of treat-to-target therapy in early rheumatoid arthritis: associations to future good radiographic and physical outcomes. Ann Rheum Dis, 77, s. 1425-25.[3]Terslev L, Naredo E, Aegerter P, et al. (3 2017). Scoring ultrasound synovitis in rheumatoid arthritis: a OMERACT-EULAR ultrasound taskforce-Part 2: reliability and application to multiple joints of a standardised consensus-based scoring system. RMD Open.[4]Andersen JKH, Pedersen JS, Laursen MS, et al. Neural networks for automatic scoring of arthritis disease activity on ultrasound images. RMD Open 2019; 5:e000891. doi:10.1136/ rmdopen-2018-000891Disclosure of Interests:None declared

scholarly journals Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

2021 ◽  
Vol 13 ◽  
Author(s):  
Robert Logan ◽  
Brian G. Williams ◽  
Maria Ferreira da Silva ◽  
Akash Indani ◽  
Nicolas Schcolnicov ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Neural Networks ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Ensemble Learning ◽  
Convolutional Neural Networks ◽  
Unmet Need ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
Interdisciplinary Approaches

Recent advancements in deep learning (DL) have made possible new methodologies for analyzing massive datasets with intriguing implications in healthcare. Convolutional neural networks (CNN), which have proven to be successful supervised algorithms for classifying imaging data, are of particular interest in the neuroscience community for their utility in the classification of Alzheimer’s disease (AD). AD is the leading cause of dementia in the aging population. There remains a critical unmet need for early detection of AD pathogenesis based on non-invasive neuroimaging techniques, such as magnetic resonance imaging (MRI) and positron emission tomography (PET). In this comprehensive review, we explore potential interdisciplinary approaches for early detection and provide insight into recent advances on AD classification using 3D CNN architectures for multi-modal PET/MRI data. We also consider the application of generative adversarial networks (GANs) to overcome pitfalls associated with limited data. Finally, we discuss increasing the robustness of CNNs by combining them with ensemble learning (EL).

Early Detection of Plant Leaf Disease Using Convolutional Neural Networks

2021 ◽  
Author(s):  
Sreenivas Pratapagiri ◽  
Rekha Gangula ◽  
Ravi G ◽  
B Srinivasulu ◽  
B Sowjanya ◽  
...  
Keyword(s):  
Neural Networks ◽  
Early Detection ◽  
Convolutional Neural Networks ◽  
Plant Leaf ◽  
Leaf Disease

scholarly journals Evaluation of Texture Maps as Input to Extract Deep Features in Glaucoma Diagnosis

2020 ◽  
Author(s):  
Daniel Silva ◽  
Romuere Silva
Keyword(s):  
Neural Networks ◽  
Early Detection ◽  
Adverse Effects ◽  
Convolutional Neural Networks ◽  
Single Channel ◽  
State Of The Art ◽  
Automatic Detection ◽  
Retinal Images ◽  
Glaucoma Diagnosis ◽  
The World

Glaucoma is a significant cause of blindness in the world. Doctors use computerized images to detect these diseases. Early detection of the disease increases the chances of treatment, reducing the adverse effects. This work proposes an evaluation of texture maps combinations as input to Convolutional Neural Networks for glaucoma classification in retinal images. In our experiments, we used three textures maps, three CNN architectures, and three classifiers. We achieve a Kappa =0.708±0.054 and a Accuracy = 0.859±0.021. We conclude that using the combination of texture maps can improve the automatic detection of glaucoma compared to single-channel inputs, and could be used by state-of-the-art methods to improve their classification rates.

scholarly journals Accurate detection of non-proliferative diabetic retinopathy in optical coherence tomography images using convolutional neural networks

2019 ◽  
Author(s):  
Mohammed Ghazal ◽  
Samr Ali ◽  
Ali Mahmoud ◽  
Ahmed Shalaby ◽  
Ayman El-Baz
Keyword(s):  
Neural Networks ◽  
Optical Coherence Tomography ◽  
Diabetic Retinopathy ◽  
Early Detection ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Imaging Modality ◽  
Detection Algorithm ◽  
Optical Coherence ◽  
Cad System

AbstractDiabetic retinopathy (DR) is a disease that forms as a complication of diabetes, It is particularly dangerous since it often goes unnoticed and can lead to blindness if not detected early. Despite the clear importance and urgency of such an illness, there is no precise system for the early detection of DR so far. Fortunately, such system could be achieved using deep learning including convolutional neural networks (CNNs), which gained momentum in the field of medical imaging due to its capability of being effectively integrated into various systems in a manner that significantly improves the performance. This paper proposes a computer aided diagnostic (CAD) system for the early detection of non-proliferative DR (NPDR) using CNNs. The proposed system is developed for the optical coherence tomography (OCT) imaging modality. Throughout this paper, all aspects of deployment of the proposed system are studied starting from the preprocessing stage required to extract input data to train the CNN without resizing the image, to the use of transfer learning principals and how best to combine features in order to optimize performance. A novel patch extraction framework for preprocessing is presented, followed by fovea detection algorithm, in addition to investigating the various CNN parameters for optimal deployment. Optimum CNN parameters and promising results are achieved. To the best of our knowledge, this is the first CNN-based DR early detection CAD system for OCT images. It achieves a promising accuracy of 94% with transfer learning.

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