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LesionScanNet: dual-path convolutional neural network for acute appendicitis diagnosis

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Abstract

Acute appendicitis is an abrupt inflammation of the appendix, which causes symptoms such as abdominal pain, vomiting, and fever. Computed tomography (CT) is a useful tool in accurate diagnosis of acute appendicitis; however, it causes challenges due to factors such as the anatomical structure of the colon and localization of the appendix in CT images. In this paper, a novel Convolutional Neural Network model, namely, LesionScanNet for the computer-aided detection of acute appendicitis has been proposed. For this purpose, a dataset of 2400 CT scan images was collected by the Department of General Surgery at Kanuni Sultan Süleyman Research and Training Hospital, Istanbul, Turkey. LesionScanNet is a lightweight model with 765 K parameters and includes multiple DualKernel blocks, where each block contains a convolution, expansion, separable convolution layers, and skip connections. The DualKernel blocks work with two paths of input image processing, one of which uses 3 × 3 filters, and the other path encompasses 1 × 1 filters. It has been demonstrated that the LesionScanNet model has an accuracy score of 99% on the test set, a value that is greater than the performance of the benchmark deep learning models. In addition, the generalization ability of the LesionScanNet model has been demonstrated on a chest X-ray image dataset for pneumonia and COVID-19 detection. In conclusion, LesionScanNet is a lightweight and robust network achieving superior performance with smaller number of parameters and its usage can be extended to other medical application domains.

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Authors and Affiliations

  1. Electrical and Electronics Engineering Department, Çukurova University, 01330, Adana, Turkey

    Muhab Hariri

  2. Biomedical Engineering Department, Çukurova University, 01330, Adana, Turkey

    Ahmet Aydın

  3. General Surgery Department, Derik State Hospital, 47800, Mardin, Turkey

    Osman Sıbıç

  4. General Surgery Department, Kanuni Sultan Suleyman Research and Training Hospital, 34303, Istanbul, Turkey

    Erkan Somuncu

  5. General Surgery Department, Bilkent City Hospital, 06800, Ankara, Turkey

    Serhan Yılmaz

  6. Interventional Radiology Department, Kanuni Sultan Suleyman Research and Training Hospital, 34303, Istanbul, Turkey

    Süleyman Sönmez

  7. Section for Fisheries Technology, Institute of Aquatic Resources, DTU Aqua, Technical University of Denmark, 9850, Hirtshals, Denmark

    Ercan Avşar

Authors
  1. Muhab Hariri
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  2. Ahmet Aydın
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  3. Osman Sıbıç
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  4. Erkan Somuncu
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  5. Serhan Yılmaz
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  6. Süleyman Sönmez
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  7. Ercan Avşar
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Corresponding author

Correspondence to Ercan Avşar.

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There are no competing interests and no funding related to this work.

Ethical approval

The study is approved by the ethical committee of the Kanuni Sultan Suleyman Research and Training Hospital (Approval Date: 11/07/2024, Reference No: 2024.07.157).

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The need for informed consent is waived by the committee due to the retrospective nature of archived datasets and fully anonymized personal information.

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Hariri, M., Aydın, A., Sıbıç, O. et al. LesionScanNet: dual-path convolutional neural network for acute appendicitis diagnosis. Health Inf Sci Syst 13, 3 (2025). https://doi.org/10.1007/s13755-024-00321-7

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  • DOI: https://doi.org/10.1007/s13755-024-00321-7

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