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LSNet: a novel CNN architecture to identify wrist fracture from a small X-ray dataset

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Abstract

Accurate classification of bone fractures from medical images plays a crucial role in effective treatment and patient care, particularly in the case of wrist X-rays. However, the limited availability of labeled medical images poses a significant challenge in training deep learning models for fracture classification. In this paper, we proposed a novel lightweight convolutional neural network (CNN) architecture, LSNet, designed specifically for Siamese networks to classify bone fractures from a limited dataset of 193 wrist X-ray images. The Siamese network extracted features from the input images, which are then fed to machine learning classifiers such as Support Vector Machine, Logistic Regression, K-Nearest Neighbor, and Decision Tree for classification. We compared the performance of our proposed LSNet against existing lightweight CNN architectures, namely SqueezeNet, ShuffleNet, MnasNet, MobileNet, and DenseNet, all implemented in PyTorch. Our results demonstrated that LSNet is not only lighter than these existing models but also achieved superior performance, with the highest accuracy and F1 score of 85.6% and 85%, respectively. This study highlights the potential of LSNet as an efficient and accurate solution for bone fracture classification in clinical settings, particularly when dealing with limited medical image datasets.

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Data Availability Statement

The data used in the methodology of this study is available in the Mendeley Data repository (https://data.mendeley.com/datasets/xbdsnzr8ct/1).

Code availability

Code is publicly available at https://github.com/talhaanwarch/SIAMESE-classifier.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. New Zealand College of Chiropractic, Auckland, New Zealand

    Talha Anwar

  2. Department of Food Science & Technology, MNS-University of Agriculture, Multan, Pakistan

    Hassan Anwar

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  1. Talha Anwar
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  2. Hassan Anwar
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Correspondence to Hassan Anwar.

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Anwar, T., Anwar, H. LSNet: a novel CNN architecture to identify wrist fracture from a small X-ray dataset. Int. j. inf. tecnol. 15, 2469–2477 (2023). https://doi.org/10.1007/s41870-023-01311-w

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  • DOI: https://doi.org/10.1007/s41870-023-01311-w

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