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. 2024 Dec 28;14(1):31104.
doi: 10.1038/s41598-024-82299-6.

Fusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features

G Elangovan  1 , N Jagadish Kumar  1 , J Shobana  1 , M Ramprasath  1 , Gyanendra Prasad Joshi  2 , Woong Cho  3
Affiliations

Fusion of transfer learning with nature-inspired dandelion algorithm for autism spectrum disorder detection and classification using facial features

G Elangovan et al. Sci Rep. .

Abstract

Autism spectrum disorder (ASD) is a neurologic disorder considered to cause discrepancies in physical activities, social skills, and cognition. There is no specific medicine for treating this disorder; early intervention is critical to improving brain function. Additionally, the lack of a clinical test for detecting ASD makes diagnosis challenging. To regulate identification, physicians entertain the children's activities and growing histories. The human face is employed as a biological signature as it has the potential reflections of the brain. It is utilized as a simpler and more helpful tool for early detection. Artificial intelligence (AI) algorithms in medicinal rehabilitation and diagnosis can help specialists identify various illnesses more successfully. However, owing to its particular heterogeneous symptoms and complex nature, diagnosis of ASD remains to be challenging for investigators. This work presents a Fusion of Transfer Learning (TL) with the Dandelion Algorithm for Accurate Autism Spectrum Disorder Detection and Classification (FTLDA-AASDDC) method. The FTLDA-AASDDC technique detects and classifies autism and non-autism samples using facial images. To accomplish this, the FTLDA-AASDDC technique utilizes a bilateral filter (BF) approach for noise elimination. Next, the FTLDA-AASDDC technique employs a fusion-based TL process comprising three models, namely MobileNetV2, DenseNet201, and ResNet50. Moreover, the attention-based bi-directional long short-term memory (A-BiLSTM) method is used to classify and recognize ASD. Finally, the Dandelion Algorithm (DA) is employed to optimize the parameter tuning process, improving the efficacy of the A-BiLSTM technique. A wide range of simulation analyses is performed to highlight the ASD classification performance of the FTLDA-AASDDC technique. The experimental validation of the FTLDA-AASDDC technique portrayed a superior accuracy value of 97.50% over existing techniques.

Keywords: Autism spectrum disorder; Bilateral filter; Dandelion algorithm; Facial image; Fusion process; Transfer learning.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Workflow of the FTLDA-AASDDC methodology.
Fig. 2
Fig. 2
Workflow of the MobileNet-V2 methodology.
Fig. 3
Fig. 3
Workflow of the DenseNet201 approach.
Fig. 4
Fig. 4
Workflow of the DenseNet201 approach.
Fig. 5
Fig. 5
Sample Images (a) Autistic (b) Non-Autistic.
Fig. 6
Fig. 6
Confusion matrices of FTLDA-AASDDC technique (a-f) Epochs 500–3000.
Fig. 7
Fig. 7
Average of FTLDA-AASDDC technique (a-f) Epochs 500–3000.
Fig. 8
Fig. 8
formula image curve of FTLDA-AASDDC technique (a-f) Epochs 500–3000.
Fig. 9
Fig. 9
Loss curve of FTLDA-AASDDC technique (a-f) Epochs 500–3000.
Fig. 10
Fig. 10
PR curve of FTLDA-AASDDC technique (a-f) Epochs 500–3000.
Fig. 11
Fig. 11
ROC curve of FTLDA-AASDDC technique (a-f) Epochs 500–3000.
Fig. 12
Fig. 12
Comparative outcome of FTLDA-AASDDC technique with recent methods.
Fig. 13
Fig. 13
PT outcome of FTLDA-AASDDC technique with recent models.
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