Detection and Classification of Food Consumption Using Convolutional Neural Networks

Food monitoring and nutritional analysis assumes a main part in health related issues, it is getting more essential in our everyday lives. In this paper, we apply a convolutional neural network (CNN) to the task of detecting and recognizing food pictures. In light of the wide variety of kinds of food, image recognition of food items is commonly extremely troublesome. In any case, deep learning has been demonstrated as of late to be an extremely ground-breaking image recognition technique, and CNN is a best in class way to deal with deep learning. We applied CNN to the undertakings of food detection and recognition through boundary enhancement. Highlights learned by Convolutional Neural Networks (CNNs) have been perceived to be more robust and expressive than hand-created ones. They have been effectively utilized in various PC vision tasks, for example, object discovery, pattern recognition and picture understanding.