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TrachomaNet: Detection and grading of trachoma using texture feature based deep convolutional neural network

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Abstract

Trachoma is the leading bacterial infectious cause of blindness worldwide. Examination for clinical signs of trachoma involves careful inspection of the lashes, cornea, eversion of the upper lid, and the tarsal conjunctiva. In this paper, we present a system for automatic detection and grading of trachoma using deep convolutional network. Salient texture features that account for the symptom of the disease are extracted from the eye image using Gabor filters. Then, a texture feature based deep convolutional neural network is used for classification. A 4-way Softmax is used for grading into a specific class (normal, trachomatous scarring, trachomatous trichiasis, and corneal opacity). Although deep learning systems are known to extract and learn features from raw image, we also show that extracting characteristic features still improves the learning capability of deep learning systems. Our model is found to be faster to train and has smaller model size as compared to state-of-the-art models such as AlexNet and GoogLeNet. Furthermore, the model achieved a diagnosis accuracy of 97.9% for detecting and grading trachoma, which improves the accuracies obtained by AlexNet and GoogLeNet by 10% and 3%, respectively.

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Data Availability

The image dataset utilized for training, validation, and test was acquired from St. Paul Hospital and Carter Center Ethiopia, Ethiopia. This dataset is not publicly available, and restrictions apply to their use.

Code Availability

The code for feature extraction and learning includes intellectual property and cannot be released publicly.

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Authors and Affiliations

  1. Department of Software Engineering, Debre Berhan University, Debre Berhan, Ethiopia

    Belesti Yenegeta

  2. Department of Computer Science, Addis Ababa University, Addis Ababa, Ethiopia

    Yaregal Assabie

Authors
  1. Belesti Yenegeta
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  2. Yaregal Assabie
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Contributions

B.Y.: Writing original draft, research and experimental planning, deep learning system design, experiments, results analysis, manuscript writing, and revised manuscript; Y.A.: Writing: review and editing, texture feature based deep learning design, writing parts of the manuscript, and revised manuscript

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Correspondence to Belesti Yenegeta.

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Appendix: Sample Dataset for CO, TT, TS, and Normal cases from Top to Bottom, respectively

Appendix: Sample Dataset for CO, TT, TS, and Normal cases from Top to Bottom, respectively

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Yenegeta, B., Assabie, Y. TrachomaNet: Detection and grading of trachoma using texture feature based deep convolutional neural network. Multimed Tools Appl 82, 4209–4234 (2023). https://doi.org/10.1007/s11042-022-13214-2

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  • DOI: https://doi.org/10.1007/s11042-022-13214-2

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