Stacked autoencoder with novel integrated activation functions for the diagnosis of autism spectrum disorder

Autism screening is crucial for the early diagnosis of developmental disorder. The combination of machine learning (ML) and deep learning (DL) approaches are applied to produce memory efficient and less complex deep learning models for the computer aided diagnosis (CAD) of autism screening. In the p...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Neural computing & applications Ročník 35; číslo 23; s. 17043 - 17075
Hlavní autoři: M, Kaviya Elakkiya, Dejey
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.08.2023
Springer Nature B.V
Témata:
Accuracy
Artificial Intelligence
Autism
Classification
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Datasets
Deep learning
Diagnosis
Feature extraction
Image Processing and Computer Vision
Independent component analysis
Machine learning
Magnetic resonance imaging
Mathematical analysis
Original Article
Principal components analysis
Probability and Statistics in Computer Science
Screening
Tensors
Group PCA
Stacked autoencoder
Functional MRI
Group ICA
Activation function
Autism spectrum disorder
ISSN:0941-0643, 1433-3058
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Autism screening is crucial for the early diagnosis of developmental disorder. The combination of machine learning (ML) and deep learning (DL) approaches are applied to produce memory efficient and less complex deep learning models for the computer aided diagnosis (CAD) of autism screening. In the proposed work, two novel integrated activation functions such as Li-ReLU and S-RReLU are developed to aid in the classification of autistic subjects and typical controls (TC) with maximum accuracy. As functional magnetic resonance imaging (fMRI) data is noisy, it undergoes temporal and spatial pre-processing. The artifact free high dimensional fMRI data is exercised for the process of feature extraction and dimensionality reduction employing group principal component analysis (Group PCA) and group independent component analysis (Group ICA). The selected features are normalized using 0–1 normalization and converted to tensors. Stacked autoencoder (SAE) utilizes the fMRI tensor data for the classification of autism spectrum disorder (ASD) subjects and typical controls. The proposed work is implemented and tested on all datasets of ABIDE I database. The validation accuracy of CMU_a, KKI, UCLA_2, OLIN, Yale and NYU datasets are obtained as 100, 80, 71.43, 100, 85.71 and 93.33% using novel Li-ReLU activation function in the proposed system. With the help of new activation function called S-RReLU, the proposed system achieves validation accuracy of about 10, 100, 57.14, 100, 78.57 and 93.33% for CMU_a, KKI, UCLA_2, OLIN, Yale and NYU datasets. Thus, the proposed method outperforms all other existing state-of-the-art works in terms of accuracy.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08565-2