Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being
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A. Reethika
Abstract
Deep learning just surpassed the advancement in the process of food image identification. Nutrient, a novel deep learning architecture and a classification solution of image pixel classification of healthy or unhealthy food, is described in this research. This work is mainly focused for physically challenged people of their food consumption. Some people cannot be able to know whether the food is good to their health or not; hence, we proposed a method to recognize the food in a proper way. The new framework was made to approximate nutrition of the food at the image level using food-pics dataset that contains nutrient images. Convolutional neural networks, a deep learning approach that has been used successfully in image recognition and classification tasks, have been trained with nutrition image training data, and a high classification success value has been achieved. The arithmetic average nutritional values of the five highest predicted nutritional results obtained from the net were taken as the approximate nutrient content. These help to recognize the nutrition of food by the physically challenged people by themselves without any help of others.
Abstract
Deep learning just surpassed the advancement in the process of food image identification. Nutrient, a novel deep learning architecture and a classification solution of image pixel classification of healthy or unhealthy food, is described in this research. This work is mainly focused for physically challenged people of their food consumption. Some people cannot be able to know whether the food is good to their health or not; hence, we proposed a method to recognize the food in a proper way. The new framework was made to approximate nutrition of the food at the image level using food-pics dataset that contains nutrient images. Convolutional neural networks, a deep learning approach that has been used successfully in image recognition and classification tasks, have been trained with nutrition image training data, and a high classification success value has been achieved. The arithmetic average nutritional values of the five highest predicted nutritional results obtained from the net were taken as the approximate nutrient content. These help to recognize the nutrition of food by the physically challenged people by themselves without any help of others.
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
Chapters in this book
- Frontmatter I
- Contents V
- List of Authors VII
- The Learning of Deep Learning: Overview, Methods, and Applications 1
- Foundation of Cognitive Computing 19
- Applications and Implications of Artificial Intelligence and Deep Learning in Computer Vision 35
- A Study of Voice Recognition System Using Deep Learning Techniques 53
- Building Machine Learning–Based Prediction System for Critical Diseases 75
- An Overview of Internet of Things and Machine Learning for Smart Healthcare 97
- Nutrition Food Recognition Using Deep Learning Algorithm for Physically Challenged Human Being 113
- Healthcare Data Analysis Using Deep Learning Paradigm 129
- Cognitive Authentication for Smart Healthcare System 149
- Cognitive-Inspired Computer Vision Assist System for Diabetic Retinopathy Detection from Fundus Images 165
- A Novel Deep Belief Neural Network Model for Abstractive Text Summarization 179
- Index 201
