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Development of a Machine Learning-Based Calorie Estimation Model for Uncooked Nigerian Foods

Domain:

healthcare

Record type:

model
Creator:
Osunade, O.AjiAsoADE
Publisher:
Fac
Host:avatar

The assessment of food intake is an important aspect in the promotion of healthy living, particularly in Nigeria, where
the challenges that exist in the estimation of the energy value of food consumed have led to the increase of lifestyle
diseases such as obesity, diabetes, and heart-related problems. This research aimed at addressing the problem of food
estimation through the creation of a machine learning model for the estimation of the calories contained in raw food
consumed in Nigeria. The model was developed based on the use of a wide range of food items, 184, which exist in
Nigeria. These food items were used, rotated, flipped, and zoomed to improve the accuracy of the model. The CNN
algorithm was used for the classification. The accuracy of the model was tested using the Mean Absolute Error, Mean
Square Error, and R-square value. The model achieved an R-square value of 0.99. The accuracy of the model was
validated based on the existing studies that have been conducted on the estimation of calories through the use of
images of food. The model developed can be used for the control of diet for patients on regulated nutrition.

Visit

doi.org

Tasks

computer visionimage classification

Tags

calories estimationcaloriesRegional Convolutional Neural Network (R-CNN)Diet managementImage detectionDeep Learning

Licenses

info:eu-repo/semantics/openAccessOther (Not Open)

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