Analysis of Convolutional Neural Networks on Indian food detection and estimation of calories

Sathish, Suriyakrishnan; Ashwin, S.; Quadir, Abdul; Pavithra, L.

doi:10.1016/j.matpr.2022.03.122

Cited by 18 publications

(4 citation statements)

References 15 publications

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“…Similar trends can also be seen in volume estimation frameworks [78], [115] and nutrition estimation frameworks [91], [92], [132], [133]. The most noticeable trend in using Deep Learning methods such as RCNN [115], OpenCN CNN [118], MobileNetV2 [119], and so on can be seen in food classification systems from 2014 to 2023. Researchers are enthusiastic about utilizing DL techniques because of their capability to learn directly from food images.…”

Section: Discussionmentioning

confidence: 57%

“…In recent years, especially in 2022, we have observed some studies conducted by [9] and [115] where the researchers attempted to increase the efficiency of their frameworks by utilizing optimization techniques such as Particle Swarm Optimization, Genetic Algorithm, Bayesian Fuzzy Clustering, etc. In both 2022 and 2023, we still observe the utilization of deep CNN-based frameworks such as DCNN [118], transfer learning CNN [116], ResNet50 [117], MobileNet V2 [119], deep CNN-based Progressive Region Enhancement Network [59], etc. for recognizing different food classes.…”

Section: ) Deep Learning Methodsmentioning

confidence: 99%

“…[118], [130], [132], [133] also achieve high performance for calorie estimation, between the accuracies of 94% [132] and 98.5% [130] and an error variation of ±10 calories [118]. We also observe that additional techniques are used to improve the performance of the food nutrition estimation systems, such as mean shift segmentation, visual saliency in [130], OpenCV in [118], normalizing arithmetic mean and harmonic mean in [132]. Lately, in a study by Hu et al [131], Near Infrared Spectroscopy (NIRS) technology has been used to estimate the calories of food images.…”

Section: Nutrition Estimationmentioning

confidence: 99%

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A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

et al. 2023

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Monitoring nutritional values in food can help an individual in planning a healthy diet. In addition, regular dietary assessment can improve and maintain the physical and mental health of individuals. Recent advancement in computer vision using Deep Learning has enabled researchers to develop various techniques for automatic food nutrition estimation frameworks. Researchers have also contributed to prepare large food image datasets consisting of various food classes for this purpose. However, automatic estimation of nutritional values from food images still remains a challenging task. This review paper critically analyzes and summarizes existing methodologies and datasets used for automated estimation of nutritional value from food images. We first define the taxonomies in order to categorize the existing research works. Then, we study different methods to detect the food value estimation from food images in those categories. We have critically analyzed existing methods and compared the performance of various approaches for estimating food value using conventional performance metrics such as Accuracy, Error Rate, Intersection over Union (IoU), Sensitivity, Specificity, Precision, etc. In particular, we emphasize the current trends and techniques of Deep Learning-based approaches for food value estimation from images. Moreover, we have identified the ongoing challenges associated with automated food estimation systems and outlined the potential future directions. This review can immensely benefit researchers and practitioners, including computer scientists, health practitioners, and nutritionists.

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Section: Discussionmentioning

confidence: 57%

Section: ) Deep Learning Methodsmentioning

confidence: 99%

Section: Nutrition Estimationmentioning

confidence: 99%

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A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

et al. 2023

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“…Several techniques for optimizing the network have been proposed in an Inceptionv3 model to loosen the constraints for simpler model adaptation. Factorized convolutions, regularization, dimension reduction, and parallelized computations are among the methods used [45].…”

Section: Inceptionv3 Frameworkmentioning

confidence: 99%

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Deshpande,

Gite,

Pradhan

2024

International Journal on Smart Sensing and Intelligent Systems

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In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-16 and Inception, using transfer learning. It aims to accurately classify leukemia subtypes using real and standard dataset images, focusing on interpretability. Therefore, the use of Local Interpretable Model-Agnostic Explanations (LIME) is employed to achieve interpretability. The ensemble model achieves an accuracy of 83.33% in binary classification, outperforming individual models. In multi-class classification, VGG-16 and Inception reach accuracies of 83.335% and 93.33%, respectively, while the ensemble model reaches an accuracy of 100%.

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Carbonic Acid Gas Emission Rating by Vehicles Using Datascience Techniques

Prathiksha,

Joshua,

Abdul Quadir

et al. 2023

Lecture Notes in Networks and Systems

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Analysis of Convolutional Neural Networks on Indian food detection and estimation of calories

Cited by 18 publications

References 15 publications

A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

A Study on Food Value Estimation From Images: Taxonomies, Datasets, and Techniques

Explainable AI for binary and multi-class classification of leukemia using a modified transfer learning ensemble model

Carbonic Acid Gas Emission Rating by Vehicles Using Datascience Techniques

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