Autism Spectrum Disorder Detection Using MobileNet

被引：0

作者：

Arvapalli, Surya Teja ^{[1
]}

Abhay, Sai A. ^{[1
]}

Mounika, D. ^{[1
]}

Pujitha, Vani M. ^{[1
]}

机构：

[1] VR Siddhartha Engn Coll, Vijayawada, India

来源：

INTERNATIONAL JOURNAL OF ONLINE AND BIOMEDICAL ENGINEERING | 2022年 / 18卷 / 10期

关键词：

CNN; transfer learning; autism; classification; SYSTEM;

D O I：

10.3991/ijoe.v18i10.31415

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Spectrum Illness (ASD), an evolution of the brain disorder, is commonly related with sensory difficulties, such as excessive or insufficient sensitivity to sounds, scents, or touch. Autism Spectrum Disorder (ASD) is evolving at a faster rate than ever before. By screening tests autism detection is very expensive and time consuming. With the advancement of Deep Learning (DL),autism can be predicted from a young age. In this paper we are using Convolutional Neural Network (CNN) with Transfer Learning (TL) models to classify the disease and we will suggest the precautions if it is detected as autism. Here we consider the Autism Master Dataset (AMD) from kaggle.com website, which contains two classes (Autism, Non_Autism). By using this models, we are obtaining good accuracy.

引用

页码：129 / 142

页数：14

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