Diagnosis of periventricular leukomalacia in children with artificial intelligence-based models developed using brain magnetic resonance images | Signal, Image and Video Processing
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Diagnosis of periventricular leukomalacia in children with artificial intelligence-based models developed using brain magnetic resonance images

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Abstract

Periventricular leukomalacia is periventricular white matter damage that develops due to hypoxia and ischemia of the brain. It is one of the leading causes of neurological and developmental problems in children that will affect their future lives. Therefore, the correct diagnosis is important for giving the appropriate treatment. The main imaging method used in the diagnosis is magnetic resonance imaging (MRI). In this study, we evaluated the detectability of periventricular leukomalacia with artificial intelligence models in MRIs in children. In the study, two new artificial intelligence-based models are proposed to classify brain MRIs. The first proposed model consists of 19 layers, and this new model was more successful than previously trained deep models for classifying MRIs. In addition, the number of layers is lower than the models accepted in the literature. In the second model we proposed, the features were taken from our first model and optimized with the neighborhood component analysis (NCA) method, and then classified in the wide neural network. The accuracy values obtained in the models we have proposed are 94.62 and 98.92%, respectively. These accuracy values show that our proposed model is successful in classifying MRIs.

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The data used in the article will be shared upon request.

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Funding

There is no funding support in the article.

Author information

Authors and Affiliations

  1. Department of Radiology, Firat University School of Medicine, Elazig, Turkey

    Yesim Eroglu

  2. Computer Engineering Department, Malatya Turgut Ozal University, Malatya, Turkey

    Muhammed Yildirim

  3. Computer Engineering Department, Firat University, Elazig, Turkey

    Ahmet Cinar

Authors
  1. Yesim Eroglu
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  2. Muhammed Yildirim
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  3. Ahmet Cinar
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Contributions

The dataset used in the study is Created by Yesim Eroglu. In the continuation of the article, the contribution of the authors is equal. All authors reviewed the manuscript.

Corresponding author

Correspondence to Muhammed Yildirim.

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Conflict of interest

The authors declared that no conflict of interest.

Ethical approval

The dataset used in the study was obtained from Firat University, Department of Radiology. Approval was obtained from the ethics committee of the university for the study (session date: 23.09.2021; number of sessions: 2021/10–01).

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Eroglu, Y., Yildirim, M. & Cinar, A. Diagnosis of periventricular leukomalacia in children with artificial intelligence-based models developed using brain magnetic resonance images. SIViP 17, 4543–4550 (2023). https://doi.org/10.1007/s11760-023-02689-7

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  • DOI: https://doi.org/10.1007/s11760-023-02689-7

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