Abstract
In recent years, obesity and health of diabetic patients have become major issues. To address these issues, it is very important to know the intake of calories, carbohydrates, and sugar. We propose a novel deep learning convolutional neural network based image recognition system that can run on Android smartphones that not only provides the appropriate nutritional estimates to users after passing a food image as input but also suggests alternative food recipes for diabetic patients. We have implemented transfer learning as well as fine-tuning and our CNN model was able to achieve comparatively higher accuracy than other approaches that used a similar setup on the Food-101 dataset. By user experiments and approval from well-known doctors, effectiveness of the proposed system was confirmed. The future scope includes expanding to more food categories and optimizing the model for better results.
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Acknowledgements
We would like to thank Doctor Harshal Joshi, M. D. and Doctor Sanjay Gulhane, M. D. for their support and guidance.
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Merchant, K., Pande, Y. (2019). ConvFood: A CNN-Based Food Recognition Mobile Application for Obese and Diabetic Patients. In: Shetty, N., Patnaik, L., Nagaraj, H., Hamsavath, P., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Advances in Intelligent Systems and Computing, vol 882. Springer, Singapore. https://doi.org/10.1007/978-981-13-5953-8_41
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DOI: https://doi.org/10.1007/978-981-13-5953-8_41
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