Accuracy Analysis for Image Classification and Identification of Nutritional Values Using Convolutional Neural Networks in Comparison with Logistic Regression Model

Abstract

Aim: The goal is to raise public awareness about nutritional issues by using food images to predict nutritional analysis using a novel image classification technique. Methods and Materials: The proposed research will be conducted at our university, and a total of two groups have been formed. There are two types of neural networks: a convolutional neural network and a logistic regression network. The framework uses 10 samples per group to evaluate accuracy. Gpower of 80% was used to calculate the sample size. Results: Convolutional Neural Network algorithm has predicted the nutritional analysis with the accuracy of (83.84%) which is more compared with the Logistic algorithm (72.3%) in identifying the fruit, Calorie count, amount of protein content, total fat, and subsequently carbohydrates measurement and so on. There is no statistically significant difference with (P = 0.092, >.05) among the classification algorithms. Conclusion: The analysis shows that the Convolutional Neural Network is significantly better for the whole Nutrition Analysis process compared to the Logistic regression.