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Deep Neural Networks for Brain Tumor Detection from MRI Images

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Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 348))

Abstract

Brain tumor patients have a significant mortality rate. If the tumors are misdiagnosed, it may result in ineffective medical treatment and reduce their life chances. As the risk of brain tumors increases with age and the world’s population ages, there is an urgent need to develop low-cost, easy-to-use early detection technologies. MRI scans are commonly used to visualize a patient’s brain. Artificial intelligence (AI), deep learning (DL), and its sub-domain have recently reduced the need for human judgment in detecting disorders. DL models are increasingly being employed in traditional supervised learning algorithms due to their inherent advantages of automatically obtaining the required features from images. The detection of brain tumors is one of the most challenging tasks in biomedical imaging. This study is intended to propose a deep neural network (DNN) based solution with a limited number of epochs and parameters. The experiment was conducted on two different datasets, and the proposed DNN obtained 99.22% accuracy, 98.94% sensitivity, 99.53% specificity, 99.57% precision, and 99.26% F1-Score for Dataset (D1) and 99.43% accuracy, 98.86% sensitivity, 100.0% specificity, 100.0% precision, and 99.43% F1-Score for dataset (D2). The results are comparable with the current state-of-the-art.

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh

    Md. Kawsher Mahbub, Milon Biswas & Md. Abdul Mozid Miah

  2. Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, 1342, Bangladesh

    M. Shamim Kaiser

Authors
  1. Md. Kawsher Mahbub
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  2. Milon Biswas
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  3. Md. Abdul Mozid Miah
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  4. M. Shamim Kaiser
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Editor information

Editors and Affiliations

  1. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  2. Amity University Rajasthan, Jaipur, India

    Kanad Ray

  3. Surface Characterization Group, National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  4. Faculty of Applied Sciences and Technology, Tun Hussein Onn University of Malaysia, Parit Raja, Johor, Malaysia

    Kavikumar Jacob

  5. Faculty of Applied Sciences and Technology (FAST), Universiti Tun Hussein Onn Malaysia, Johor, Malaysia

    Kek Sie Long

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Mahbub, M.K., Biswas, M., Miah, M.A.M., Kaiser, M.S. (2022). Deep Neural Networks for Brain Tumor Detection from MRI Images. In: Kaiser, M.S., Ray, K., Bandyopadhyay, A., Jacob, K., Long, K.S. (eds) Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 348. Springer, Singapore. https://doi.org/10.1007/978-981-16-7597-3_39

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