LENBC: Learning Embedded Neural Boost Classification for Robust Autism Classification using the Autism Image Dataset
Keywords:
Autism Detection, Image Preprocessing, Gaussian blurring, Edge Detection, Learning Embedded Neural Boost Classification (LENBC), Feature ExtractionAbstract
Understanding and supporting individuals with autism involves recognizing and respecting their unique perspectives and abilities while fostering an inclusive and accommodating environment. Ongoing research seeks to unravel the complexities of autism and enhance interventions to improve the quality of life for those affected. In the quest for advancing autism classification, this research orchestrates Learning Embedded Neural Boost Classification (LENBC) through an innovative ensemble model by leveraging the Autism Image Dataset (AID). The journey begins with meticulous image preprocessing, incorporating resizing, normalization, grayscale conversion, Gaussian blurring, and edge detection. The extracted features, derived from Neural Network architecture, serve as the foundation for subsequent classification. The convolutional layers of the CNN are designed to capture intricate patterns and nuanced information from the images, enhancing the model's ability to discern relevant features for autism classification. The CatBoost classifier, known for its robustness and efficiency, complements the CNN by making predictions based on the extracted features. This paper details the step-by-step process of this novel ensemble model, emphasizing the synergy between deep learning and boosting techniques. We delve into the intricacies of image preprocessing, feature extraction, and the unique role each model plays in the final classification. The experimental results showcase the efficacy of our approach, with an impressive accuracy of 97.42% in autism classification. The amalgamation of these cutting-edge methodologies not only propels the accuracy of autism classification but also sheds light on the potential of interdisciplinary collaboration between computer vision and machine learning. This research opens new avenues for exploring the synergy between art-inspired image processing and state-of-the-art classifiers, offering a harmonious blend of creativity and intelligence in the realm of medical image analysis.
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A. Z. Guo, (2023), "Automated Autism Detection Based on Characterizing Observable Patterns From Photos," in IEEE Transactions on Affective Computing, vol. 14, no. 1, pp. 836-841, DOI: 10.1109/TAFFC.2020.3035088.
B. Henderson, et al., (2023), "Encoding Kinematic and Temporal Gait Data in an Appearance-Based Feature for the Automatic Classification of Autism Spectrum Disorder," in IEEE Access, vol. 11, pp. 134100-134117, DOI: 10.1109/ACCESS.2023.3336861
Bal VH, et al., (2022), "Cognitive profiles of children with autism spectrum disorder with parent-reported extraordinary talents and personal strengths". Autism; 26(1):62-74. DOI:10.1177/13623613211020618
Bala, Mousumi, et al., (2022), "Efficient Machine Learning Models for Early Stage Detection of Autism Spectrum Disorder" Algorithms 15, no. 5: 166. DOI: 10.3390/a15050166
Baribeau DA, et al. (2023), "Developmental cascades between insistence on sameness behaviour and anxiety symptoms in autism spectrum disorder." Eur Child Adolesc Psychiatry. 32(11):2109-2118. DOI: 10.1007/s00787-022-02049-9
Baygin M, et al. (2021), "Automated ASD detection using hybrid deep lightweight features extracted from EEG signals". Comput Biol Med. 2021; 134:104548. DOI: 10.1016/j.compbiomed.2021.104548
H. Zhu, et al., (2023), "Contrastive Multi-View Composite Graph Convolutional Networks Based on Contribution Learning for Autism Spectrum Disorder Classification," in IEEE Transactions on Biomedical Engineering, vol. 70, no. 6, pp. 1943-1954, DOI: 10.1109/TBME.2022.3232104
Hasan Alkahtani, Theyazn H. H. Aldhyani and Mohammed Y. Alzahrani. (2023), "Early Screening of Autism Spectrum Disorder Diagnoses of Children Using Artificial Intelligence". JDR, 2(1):14-25. DOI: 10.57197/JDR-2023-0004
Hodges, H., et al., (2020), "Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation". Translational pediatrics, 9(Suppl 1), S55–S65. DOI: 10.21037/tp.2019.09.09
K. Devika, et al., (2022), "Outlier-Based Autism Detection Using Longitudinal Structural MRI," in IEEE Access, vol. 10, pp. 27794-27808, DOI: 10.1109/ACCESS.2022.3157613.
Kareem, Aythem, et al., (2023), "Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network", Baghdad Science Journal. 20. 1182-1193, DOI: 10.21123/bsj.2023.8564.
M. Kunda, et al., (2023), "Improving Multi-Site Autism Classification via Site-Dependence Minimization and Second-Order Functional Connectivity," in IEEE Transactions on Medical Imaging, vol. 42, no. 1, pp. 55-65, DOI: 10.1109/TMI.2022.3203899.
Menaka R, et al., (2023), "An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG". Clinical EEG and Neuroscience.; DOI: 10.1177/15500594231178274
Milano N, et al., (2023), "A deep learning latent variable model to identify children with autism through motor abnormalities". Front. Psychol. 14:1194760. DOI: 10.3389/fpsyg.2023.1194760
S. Jain, et al., (2023), "Autism Detection of MRI Brain Images Using Hybrid Deep CNN With DM-Resnet Classifier," in IEEE Access, vol. 11, pp. 117741-117751, DOI: 10.1109/ACCESS.2023.3325701
S. Liang, et al., (2021), "Autism Spectrum Self-Stimulatory Behaviors Classification Using Explainable Temporal Coherency Deep Features and SVM Classifier," IEEE Access, vol. 9, pp. 34264-34275, DOI: 10.1109/ACCESS.2021.3061455.
S. M. Mahedy Hasan, et al., (2023), "A Machine Learning Framework for Early-Stage Detection of Autism Spectrum Disorders," in IEEE Access, vol. 11, pp. 15038-15057, DOI: 10.1109/ACCESS.2022.3232490.
S. Sarabadani, et al., (2020), "Physiological Detection of Affective States in Children with Autism Spectrum Disorder," in IEEE Transactions on Affective Computing, vol. 11, no. 4, pp. 588-600, DOI: 10.1109/TAFFC.2018.2820049.
Suman Raj, et al., (2020), "Analysis and Detection of Autism Spectrum Disorder Using Machine Learning Techniques", Procedia Computer Science, Volume 167, Pages 994-1004, ISSN 1877-0509, DOI: 10.1016/j.procs.2020.03.399.
Tariq Rafiq, et al., (2023), "Autism Spectrum Disorder Detection in Children using the Efficacy of Machine Learning Approaches", IJCSNS International Journal of Computer Science and Network Security, VOL.23 No.4, DOI: 10.22937/IJCSNS.2023.23.4.24
Wang, H., & Avillach, P. (2020), "Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants in Autism Spectrum Disorder: Genotype-Based Deep Learning". JMIR Medical Informatics, 9, DOI:10.2196/24754
Y. Liang, et al., (2021), "A Convolutional Neural Network Combined With Prototype Learning Framework for Brain Functional Network Classification of Autism Spectrum Disorder," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 2193-2202, DOI: 10.1109/TNSRE.2021.3120024.
Z. A. Huang, et al., (2021), "Identifying Autism Spectrum Disorder From Resting-State fMRI Using Deep Belief Network," in IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 7, pp. 2847-2861, DOI: 10.1109/TNNLS.2020.3007943
Z. Lu, et al., (2023), "Jointly Composite Feature Learning and Autism Spectrum Disorder Classification Using Deep Multi-Output Takagi-Sugeno-Kang Fuzzy Inference Systems," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 20, no. 1, pp. 476-488, DOI: 10.1109/TCBB.2022.3163140
Zhengning Wang, et al., (2023), "Brain functional activity-based classification of autism spectrum disorder using an attention-based graph neural network combined with gene expression", Cerebral Cortex, Volume 33, Issue 10, Pages 6407–6419, DOI: 10.1093/cercor/bhac513
M. Preetha, et al., (2024), “A Preliminary Analysis by using FCGA for Developing Low Power Neural Network Controller Autonomous Mobile Robot Navigation”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), ISSN:2147-6799, Vol:12,Issue 9s, Page No-39-42.
M. Preetha, et al., (2024), “Deep Learning-Driven Real-Time Multimodal Healthcare Data Synthesis”, International Journal of Intelligent Systems and Applications in Engineering (IJISAE), ISSN:2147-6799, Vol.12, Issue 5, page No-360-369.
M. Preetha, et al., (2023), “Efficient Re-clustering with Novel Fuzzy Based Grey Wolf Optimization for Hotspot Issue Mitigation and Network Lifetime Enhancement”, Journal of Ad Hoc & Sensor Wireless Networks, ISSN:1551-9899 (print) ISSN: 1552-0633 (online) Vol. 56, Issue 4, page No-273-297.
https://www.kaggle.com/datasets/cihan063/autism-image-data Accessed on 25th July 2023
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