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Pleural Effusion Detection Using Machine Learning and Deep Learning Based on Computer Vision

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Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022 (AISI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 152))

Abstract

Pleural effusion is one of the serious chest diseases that affect human life depending on the causes, such as malignant tumors, liver, kidney or chest failure, and the risks increase with the delay in diagnosis and treatment. In recent years, artificial intelligence (AI) has achieved a significant development in the medical field. As part of artificial intelligence, deep learning (DL), machine learning (ML), and computer vision (CV) have become very important in the diagnosis and treatment of diseases, as they help in terms of early and accurate diseases diagnosis and suggesting the best treatments. Furthermore, with the widespread use of medical images in diagnosing diseases, the need for computer vision, machine learning, and deep learning to analyze and understand those images, and to help clinicians make quick and accurate diagnoses has increased. In this paper, machine learning model i.e., artificial neural network (ANN), and deep learning models i.e., AlexNet, GoogleNet, SqueezeNet, and DarkNet19, are used to detect the presence or absence of pleural effusion in Chest X-ray14 dataset images. We have used 80% of the dataset for training the models, and the remaining 20% for testing.

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Authors and Affiliations

  1. Mathematics Department, Faculty of Science, Suez Canal University, Ismailia, Egypt

    Rehab Fathi Ibrahim, N. M. Yhiea & Alshaimaa Mostafa Mohammed

  2. Electrical Engineering Department, Faculty of Engineering, Suez Canal University, Ismailia, Egypt

    Ahmed Magdy Mohamed

Authors
  1. Rehab Fathi Ibrahim
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  2. N. M. Yhiea
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  3. Alshaimaa Mostafa Mohammed
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  4. Ahmed Magdy Mohamed
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Corresponding author

Correspondence to Rehab Fathi Ibrahim .

Editor information

Editors and Affiliations

  1. Faculty of Computers Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  2. Faculty of Electrical Engineering and Computer Science, VÅ B-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Mincong Tang

  4. College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou, Fujian, China

    Tien-Wen Sung

  5. Fujian University of Technology, New Taipei, Taiwan

    Kuo-Chi Chang

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Ibrahim, R.F., Yhiea, N.M., Mohammed, A.M., Mohamed, A.M. (2023). Pleural Effusion Detection Using Machine Learning and Deep Learning Based on Computer Vision. In: Hassanien, A.E., Snášel, V., Tang, M., Sung, TW., Chang, KC. (eds) Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022. AISI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 152. Springer, Cham. https://doi.org/10.1007/978-3-031-20601-6_19

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