A Convolutional Neural Network Model for Early-Stage Detection of Autism Spectrum Disorder

2021 ◽  
Author(s):  
Md. Fazle Rabbi ◽  
S. M. Mahedy Hasan ◽  
Arifa Islam Champa ◽  
Md. Asif Zaman
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Early Stage ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Multisite Autism Spectrum Disorder Classification Using Convolutional Neural Network Classifier and Individual Morphological Brain Networks

2021 ◽  
Vol 14 ◽  
Author(s):  
Jingjing Gao ◽  
Mingren Chen ◽  
Yuanyuan Li ◽  
Yachun Gao ◽  
Yanling Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Convolutional Neural Network ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Brain Network ◽  
Spectrum Disorder ◽  
Structural Covariance ◽  
Almost All ◽  
New Framework

Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders with behavioral and cognitive impairment and brings huge burdens to the patients’ families and the society. To accurately identify patients with ASD from typical controls is important for early detection and early intervention. However, almost all the current existing classification methods for ASD based on structural MRI (sMRI) mainly utilize the independent local morphological features and do not consider the covariance patterns of these features between regions. In this study, by combining the convolutional neural network (CNN) and individual structural covariance network, we proposed a new framework to classify ASD patients with sMRI data from the ABIDE consortium. Moreover, gradient-weighted class activation mapping (Grad-CAM) was applied to characterize the weight of features contributing to the classification. The experimental results showed that our proposed method outperforms the currently used methods for classifying ASD patients with the ABIDE data and achieves a high classification accuracy of 71.8% across different sites. Furthermore, the discriminative features were found to be mainly located in the prefrontal cortex and cerebellum, which may be the early biomarkers for the diagnosis of ASD. Our study demonstrated that CNN is an effective tool to build the framework for the diagnosis of ASD with individual structural covariance brain network.

scholarly journals Automated Detection of Autism Spectrum Disorder Using a Convolutional Neural Network

2020 ◽  
Vol 13 ◽  
Author(s):  
Zeinab Sherkatghanad ◽  
Mohammadsadegh Akhondzadeh ◽  
Soorena Salari ◽  
Mariam Zomorodi-Moghadam ◽  
Moloud Abdar ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Convolutional Neural Network ◽  
Autism Spectrum ◽  
Automated Detection ◽  
Spectrum Disorder

scholarly journals A Convolutional Neural Network Model to Differentiate Attention Deficit Hyperactivity Disorder and Autism Spectrum Disorder Based on the Resting State fMRI Data

2021 ◽  
Author(s):  
Azadeh Mozhdehfarahbakhsh ◽  
Amirsaeid Moloodi ◽  
Prasun Chakrabarti ◽  
KS Jagannatha Rao ◽  
Babak Kateb ◽  
...  
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Resting State ◽  
Autism Spectrum ◽  
Fmri Data ◽  
Hyperactivity Disorder ◽  
The Brain

Background and Objectives: Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) are the two most common neurodevelopmental disorders often with overlapping symptoms. Misdiagnosis of these disorders is the leading cause of a variety of problems including inappropriate interventions and improper treatment outcome. Over the last few years, resting state functional magnetic Resonance imaging (rs-fMRI) has received clinical attention among other beneficial brain scan techniques to extract functional connectivity in the brain. However, extracting useful information by human observation is prone to errors. Material and Methods: The above unmet need prompted us to design the present investigation to construct a convolutional neural network model with 12 layers architecture in rsFMRI data aiming to differentiate the two conditions. The rs-fMRI data was collected from the ADHD-200 and ABIDE to feed into a convolutional neural network. Over the preprocessing phase, we have removed undesirable data and coordinated the remaining to MSDL atlas to recruit 39 regions of the brain. Results: Ultimately, out results obtained a 0.92 accuracy, an AUC of 0.97 and loss of 0.17 in classification and discrimination of ADHD and ASD. Conclusion: Though cross-validity with larger datasets is deemed required, the results obtained from the present investigation suggest that convolutional neural network may serve as a beneficial tool to differentiate ADHD and ASD from relatively small fMRI datasets. This further highlights the potential application of deep neural networks for serving the above purpose.

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning (Preprint)

2021 ◽  
Author(s):  
Federica Cilia ◽  
Romuald Carette ◽  
Mahmoud Elbattah ◽  
Gilles Dequen ◽  
Jean-Luc Guérin ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Eye Tracking ◽  
Convolutional Neural Network ◽  
Visual Representation ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Computer Assisted ◽  
Maximal Information Coefficient

BACKGROUND The early diagnosis of autism spectrum disorder (ASD) is highly desirable but remains a challenging task, which requires a set of cognitive tests and hours of clinical examinations. In addition, variations of such symptoms exist, which can make the identification of ASD even more difficult. Although diagnosis tests are largely developed by experts, they are still subject to human bias. In this respect, computer-assisted technologies can play a key role in supporting the screening process. OBJECTIVE This paper follows on the path of using eye tracking as an integrated part of screening assessment in ASD based on the characteristic elements of the eye gaze. This study adds to the mounting efforts in using eye tracking technology to support the process of ASD screening METHODS The proposed approach basically aims to integrate eye tracking with visualization and machine learning. A group of 59 school-aged participants took part in the study. The participants were invited to watch a set of age-appropriate photographs and videos related to social cognition. Initially, eye-tracking scanpaths were transformed into a visual representation as a set of images. Subsequently, a convolutional neural network was trained to perform the image classification task. RESULTS The experimental results demonstrated that the visual representation could simplify the diagnostic task and also attained high accuracy. Specifically, the convolutional neural network model could achieve a promising classification accuracy. This largely suggests that visualizations could successfully encode the information of gaze motion and its underlying dynamics. Further, we explored possible correlations between the autism severity and the dynamics of eye movement based on the maximal information coefficient. The findings primarily show that the combination of eye tracking, visualization, and machine learning have strong potential in developing an objective tool to assist in the screening of ASD. CONCLUSIONS Broadly speaking, the approach we propose could be transferable to screening for other disorders, particularly neurodevelopmental disorders.

KLASIFIKASI CITRA GENUS PANTHERA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK (CNN)

2019 ◽  
Vol 24 (3) ◽  
pp. 220-228
Author(s):  
Gusti Alfahmi Anwar ◽  
Desti Riminarsih
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Input Layer ◽  
Fully Connected

Panthera merupakan genus dari keluarga kucing yang memiliki empat spesies popular yaitu, harimau, jaguar, macan tutul, singa. Singa memiliki warna keemasan dan tidak memilki motif, harimau memiliki motif loreng dengan garis-garis panjang, jaguar memiliki tubuh yang lebih besar dari pada macan tutul serta memiliki motif tutul yang lebih lebar, sedangkan macan tutul memiliki tubuh yang sedikit lebih ramping dari pada jaguar dan memiliki tutul yang tidak terlalu lebar. Pada penelitian ini dilakukan klasifikasi genus panther yaitu harimau, jaguar, macan tutul, dan singa menggunakan metode Convolutional Neural Network. Model Convolutional Neural Network yang digunakan memiliki 1 input layer, 5 convolution layer, dan 2 fully connected layer. Dataset yang digunakan berupa citra harimau, jaguar, macan tutul, dan singa. Data training terdiri dari 3840 citra, data validasi sebanyak 960 citra, dan data testing sebanyak 800 citra. Hasil akurasi dari pelatihan model untuk training yaitu 92,31% dan validasi yaitu 81,88%, pengujian model menggunakan dataset testing mendapatan hasil 68%. Hasil akurasi prediksi didapatkan dari nilai F1-Score pada pengujian didapatkan sebesar 78% untuk harimau, 70% untuk jaguar, 37% untuk macan tutul, 74% untuk singa. Macan tutul mendapatkan akurasi terendah dibandingkan 3 hewan lainnya tetapi lebih baik dibandingkan hasil penelitian sebelumnya.

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