Affiliations: [a] Department of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology, Thiruvallur, Tamil Nadu, India
| [b] Department of Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
| [c] Department of Electrical Electronics and Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
| [d] Electronics and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India
Correspondence:
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Corresponding author. P. Josephin Shermila, Department of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology, Thiruvallur, Tamil Nadu, India. E-mail: [email protected].
Abstract: Foods are very essential for living beings for providing energy, development and preserve their existence. It plays a vital role in promoting health and preventing illness. Nowadays, many people are suffered from obesity, they tend to maintain their body weight by consuming a sufficient number of calories in their routine life. In this research, a novel Modified Deep Learning-based Food Item Classification (MDEEPFIC) approach has been proposed to categorize the different food items from the dataset with their calorie values. Initially, the images are processed using the sigmoid stretching method to enhance the image quality and remove the noises. Consequently, the pre-processed images are segmented using Improved Watershed Segmentation (IWS2) algorithm. Recurrent Neural Network (RNN) is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the modified dragonfly technique for same food calorie calculation. Bidirectional Long Short-Term Memory (Bi-LSTM) is utilized to classify food products based on these pertinent aspects. Finally, using food area volume and calorie and nutrition measures based on mass value, the calorie value of the categorized food item is calculated. The efficiency of the proposed method was calculated in terms of specificity, precision, accuracy, and recall F-measure. The proposed method improves the overall accuracy of 4.99%, 8.72%, and 10.4% better than existing Deep Convolution Neural Network (DCNN), Faster Recurrent convolution neural network (FRCNN), Local Variation Segmentation based Support Vector Machine (LSV-SVM) method respectively.
