A Deep Learning-Based Model for Indian Food Image Classification

Food picture classification provides considerable advantages for the food and medical industries, making it a topic of study that is rapidly increasing. Future uses for automated food identification algorithms are anticipated to include calorie estimates and diet tracking systems. This study recommends automated deep learning-based food classification systems. This study recommends automated deep learning-based food classification systems. These networks have been shown to perform considerably better when data are given and the hyperparameters are changed, making them appropriate for usage in real-world health and medical applications. Because SVM is a lightweight network, setting it up and using it is more accessible and appealing. SVM and DT can achieve a respectable accuracy of 100% even with a few parameters. The suggested CNN network will increase the efficacy of automatically categorizing food images. The accuracy of the suggested CNN has significantly risen and is currently 99.89% due to greater network depth.