Facial Image-Based Autism Detection Using A CNN-‎ExtractedFeature Ensemble with Random Forest Classification

V. Krishnamoorthy; Cuddapah Anitha; Swathy V; M. Indirani; G. Sasi; K. Selvakumarasamy

doi:10.14419/4mmwca71

Article Details

Facial Image-Based Autism Detection Using A CNN-‎ExtractedFeature Ensemble with Random Forest Classification

Authors:

V. Krishnamoorthy | Cuddapah Anitha | Swathy V | M. Indirani | G. Sasi | K. Selvakumarasamy

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 4, pp.826-831

Document Type:

Article

Publication Date:

8/2025

Document Language:

English

Document ID (Org No.):

2001387349

pISSN:

2227-5053

DOI:

10.14419/4mmwca71

Keywords:

Abstract:

 For effective care and attention, autism spectrum disorder (ASD) must be identified early and ‎This study suggests a hybrid model that blends convolutional neural networks (CNN) and ‎random forest (RF) classifiers. The proposed model had a high classification accuracy of ‎‎91.3%, demonstrating its ability to reliably detect cases of ASD. With a precision of 90.7%, ‎the model effectively reduces false positives, and its 89.5% recall ensures excellent sensitivity ‎to actual ASD patients. The balanced F1-Score of 90.1% shows that the model performs ‎consistently in terms of both precision and recall. Additionally, the model's Area Under the ‎Curve (AUC) score of 0.94 indicates its exceptional ability to discriminate between ASD and ‎non-ASD classes. All of these results demonstrate the Hybrid CNN + RF model's strength ‎and dependability, which makes it a promising non-invasive technique for early facial feature-based ASD screening‎.

Article Details

Facial Image-Based Autism Detection Using A CNN-‎ExtractedFeature Ensemble with Random Forest Classification

Authors: V. Krishnamoorthy | Cuddapah Anitha | Swathy V | M. Indirani | G. Sasi | K. Selvakumarasamy

Publisher: Science Publishing Corporation

Journal: International Journal of Basic and Applied Sciences Vol.14 Issue 4, pp.826-831

Document Type: Article

Publication Date: 8/2025

Document Language: English

Document ID (Org No.): 2001387349

pISSN: 2227-5053

DOI: 10.14419/4mmwca71

ASD

CNN

RF

F1-Score

AUC

Issues

Authors:

V. Krishnamoorthy | Cuddapah Anitha | Swathy V | M. Indirani | G. Sasi | K. Selvakumarasamy

Publisher:

Science Publishing Corporation

Journal:

International Journal of Basic and Applied Sciences Vol.14 Issue 4, pp.826-831

Document Type:

Article

Publication Date:

8/2025

Document Language:

English

Document ID (Org No.):

2001387349

pISSN:

2227-5053

DOI:

10.14419/4mmwca71