Thai food Image recognition using convolution neural network with dropout technique

Name: Niti Natephakdee

keyword: Food Recognitions

MeSH: Computer Engineering -- 301699

Classification :.DDC: e-Theses

; Convolutional Neural Network

MeSH: Optical pattern recognition -- 301700

MeSH: Thai food -- 123372

MeSH: Pattern perception -- 29689

MeSH: Convolutions (Mathematics) -- 175259

LCSH: Computer Engineering -- 301699

LCSH: Optical pattern recognition -- 301700

LCSH: Thai food -- 123372

LCSH: Pattern perception -- 29689

LCSH: Convolutions (Mathematics) -- 175259

Abstract: One of the main causes of diabetes, heart disease, and obesity in today's world is consumption behavior. To help people prevent such diseases, the consumption advice is required for behavior changes. A study proposed in this thesis is to find out a method for recognition of Thai food image using Convolutional Neural Network (CNN) and dropout technique to recognize the type of Thai food. CNN is used for distinguishing the complexity among Thai food images, while dropout technique is used for overfitting reduction. Sample used in the experiment were 16,170 images for recognizing 52 food types. The proposed method is compared with CNN without dropout, and Nu-In Net 1.1. The comparison results showed that CNN with dropout performed the best with 96.67% accuracy.

Mae Fah Luang University. Learning Resources and Educational Media Center

Address: CHIANG RAI

Email: library@mfu.ac.th

Name: Punnarumol Temdee

Role: Advisor

Created: 2022

Modified: 2023-05-10

Issued: 2023-05-10

วิทยานิพนธ์/Thesis

application/pdf

CallNumber: e-Theses

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Computer Engineering

Grantor: Mae Fah Luang University

©copyrights Mae Fah Luang University

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