A Convolutional Neural Network for Automatic Brain Tumor Detection

Authors

  • Saeed Mohsen Department of Electronics and Communications Engineering, Al-Madinah Higher Institute for Engineering and Technology, Giza, Egypt; Department of Artificial Intelligence Engineering, Faculty of Computer Science and Engineering, King Salman International University (KSIU), South Sinai, Egypt
  • Wael Mohamed Fawaz Abdel-Rehim Department of Artificial Intelligence Engineering, Faculty of Computer Science and Engineering, King Salman International University (KSIU), South Sinai, Egypt; Department of Computer Science, Faculty of Computers and Information, Suez University, Suez, Egypt
  • Ahmed Emam Department of Artificial Intelligence Engineering, Faculty of Computer Science and Engineering, King Salman International University (KSIU), South Sinai, Egypt; Computer Science and Math Department, Faculty of Science, Menoufia University, Menoufia, Egypt
  • Hossam Mohamed Kasem Electronics and Electrical Communication Engineering Department, Faculty of Engineering, Tanta University, Tanta, Egypt

DOI:

https://doi.org/10.46604/peti.2023.10307

Keywords:

artificial intelligence, MRI, convolutional neural network, brain tumors

Abstract

Magnetic resonance imaging (MRI) combined with artificial intelligence (AI) algorithms to detect brain tumors is one of the important medical applications.  In this study, a Convolutional neural network (CNN) model is proposed to detect meningioma and pituitary, which was tested with a dataset consisting of two categories of tumors with 1,800 MRI images from several persons. The CNN model is trained via a Python library, namely TensorFlow, with an automatic tuning approach to obtain the highest testing accuracy of tumor detection. The CNN model used Python programming language in Google Colab to detect sensitivity, precision, the area under the PR and receiver operating characteristic (ROC), error matrix, and accuracy. The results show that the proposed CNN model has a high performance in the detection of brain tumors. It achieves an accuracy of 95.78% and a weighted average precision of 95.82%.

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Published

2023-04-28

How to Cite

[1]
Saeed Mohsen, Wael Mohamed Fawaz Abdel-Rehim, Ahmed Emam, and Hossam Mohamed Kasem, “A Convolutional Neural Network for Automatic Brain Tumor Detection ”, Proc. eng. technol. innov., vol. 24, pp. 15–21, Apr. 2023.

Issue

Vol. 24 (2023)

Section

Articles

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