Enhancing Food Type Recognition: A Comprehensive Study on Sequential Convolutional Neural Networks for Image Classification Accuracy

Abstract

Addressing the challenge of food recognition, this study investigates the effectiveness of sequential convolutional neural networks (CNNs) and their application in accurately identifying food items within images. The research introduces a novel CNN architecture, termed "sequential_2," tailored for food classification, achieving an accuracy of 89.84% on the Food Images (Food-101) dataset. Insights from the model's architecture, performance, and findings are discussed, emphasizing its potential in image classification tasks, particularly in the context of food recognition. This innovative approach aims to automate traditionally challenging and resource-intensive tasks associated with determining food attributes, creating taxonomies, and extracting nutrient information. The results highlight the potential of combining cutting-edge deep learning techniques with practical applications, showcasing a paradigm shift in the way we approach and automate the understanding of food through technology.