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DEEPFIC: food item classification with calorie calculation using dragonfly deep learning network

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Abstract

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 Deep Learning-based Food Item Classification (DEEPFIC) 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. RNN is used to extract features like shape, size, textures, and color. The extracted features are then normalized using the dragonfly technique. The Bi-LSTM is utilized to classify food products based on these pertinent aspects. 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 by 4.99%, 8.72%, and 10.4% better than the existing DCNN, FRCNN, and LSV-SVM methods respectively.

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Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Data Science, R.M.K. College of Engineering and Technology, Thiruvallur, Tamil Nadu, India

    P. Josephin Shermila

  2. Department of Electrical and Communication Engineering, PSN College of Engineering and Technology, Tirunelveli, Tamil Nadu, India

    A. Ahilan

  3. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamil Nadu, 600089, India

    M. Shunmugathammal

  4. Department of Electrical and Electronics Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, India

    Jawahar Marimuthu

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  1. P. Josephin Shermila
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  2. A. Ahilan
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  4. Jawahar Marimuthu
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Contributions

Study conception and design: Dr. PJS, Dr. AA, MS; data collection: Dr. AA; analysis and interpretation of results: Dr. PJS, JM draft manuscript preparation: Dr. AA; All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to P. Josephin Shermila.

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Shermila, P.J., Ahilan, A., Shunmugathammal, M. et al. DEEPFIC: food item classification with calorie calculation using dragonfly deep learning network. SIViP 17, 3731–3739 (2023). https://doi.org/10.1007/s11760-023-02600-4

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  • DOI: https://doi.org/10.1007/s11760-023-02600-4

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