Food Calorie Estimation using Convolutional Neural Network

Kasyap, V Balaji; Jayapandian, N.

doi:10.1109/icspc51351.2021.9451812

Cited by 21 publications

(3 citation statements)

References 11 publications

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“…V Balaji Kasyap et al [8] The study suggests a deep learning algorithm-based method for calculating caloric intake. The authors utilize a dataset of fast food images and extract features using the MathWorks image processing toolbox.…”

Section: Literature Surveymentioning

confidence: 99%

Food Classification and Calorie Estimation Using CNN

2024

IJSREM

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This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

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Section: Literature Surveymentioning

confidence: 99%

Food Classification and Calorie Estimation Using CNN

2024

IJSREM

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“…Other studies also explore the application of DL models for food calorie measurement. For instance, in [22], Kasyap et al uses a DL model for food calorie measurement with an error reduction of 20%. In [23], Ayon et al deploy a novel DL model on the webpage images to predict food calorie content in real time.…”

Section: Literature Reviewmentioning

confidence: 99%

Lightweight and Parameter-Optimized Real-Time Food Calorie Estimation from Images Using CNN-Based Approach

et al. 2022

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Automated object identification has seen significant progress during the last decade with close to human-level accuracy, aided by deep learning methods. With the rapid rise of obesity and other lifestyle-related diseases worldwide, the availability of fast, automated, and reliable image-based food calorie estimation is becoming a necessity. With the help of a deep learning-based automated object identification system, it is possible to introduce accurate and intelligent solutions in the form of a mobile app. However, for these kind of applications, processing speed is an important concern as the images should be processed in real time. Although plenty of studies have been conducted that focus on food image detection-based calorie estimation, there is still a lack of an image-driven, lightweight, fast, and reliable food calorie estimation system. In this paper, we propose a method based on the parameter-optimized Convolution Neural Networks (CNN) for detecting food images of regular meals using a handheld camera. Once identification process of the food items are complete, the corresponding calories and nutritional facts can be calculated using prior knowledge about the food class. Through our findings, we demonstrate that our proposed approach ensures high accuracy and can significantly simplify the existing manual calorie estimation procedures by converting them into a real-time automated process.

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“…In contrast, the most attractive and efficient ML approach currently available is deep learning (DL) because DL has significant advantages in automatically learning data representations, e.g., automatic feature extraction from images [18]. DL algorithms have been successfully applied to various food domains, including food recognition, calorie estimation, nutrient or dietary assessment and quality inspection [19][20][21]. Moreover, two studies have reported the application of DL methods in detecting and counting oysters [22] or oyster larvae [23] since 2021, but to the best of our knowledge, no study has reported the use of DL method to predict oyster freshness as well as to investigate the interpretability of DL-based models for oyster applications.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Method for Predicting Pacific Oyster (Crassostrea gigas) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information

Lu,

Yu,

Han

et al. 2023

Foods

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To achieve a non-destructive and rapid detection of oyster freshness, an intelligent method using deep learning fused with malondialdehyde (MDA) and total sulfhydryl groups (SH) information was proposed. In this study, an “MDA-SH-storage days” polynomial fitting model and oyster meat image dataset were first built. AleNet-MDA and AlxNet-SH classification models were then constructed to automatically identify and classify four levels of oyster meat images with overall accuracies of 92.72% and 94.06%, respectively. Next, the outputs of the two models were used as the inputs to “MDA-SH-storage days” model, which ultimately succeeded in predicting the corresponding MDA content, SH content and storage day for an oyster image within 0.03 ms. Furthermore, the interpretability of the two models for oyster meat image were also investigated by feature visualization and strongest activations techniques. Thus, this study brings new thoughts on oyster freshness prediction from the perspective of computer vision and artificial intelligence.

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Food Calorie Estimation using Convolutional Neural Network

Cited by 21 publications

References 11 publications

Food Classification and Calorie Estimation Using CNN

Food Classification and Calorie Estimation Using CNN

Lightweight and Parameter-Optimized Real-Time Food Calorie Estimation from Images Using CNN-Based Approach

An Intelligent Method for Predicting Pacific Oyster (Crassostrea gigas) Freshness Using Deep Learning Fused with Malondialdehyde and Total Sulfhydryl Groups Information

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