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Brain MRI Image Classification Using Deep Learning

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Applied Machine Learning and Data Analytics (AMLDA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1818))

Abstract

Brain Tumors can be lethal if not diagnosed or misdiagnosed. Seeing the mortality because of this is high there’s a need to find a solution to detect, classify and diagnose this problem effectively and help medical professionals save precious lives. Brain MRI [1] images help in detection of these tumors. Though there are classification techniques the most common limitation is low accuracy. In this paper we use Deep Learning Architectures to perform these tasks. Architectures like VGG19, ResNet50 [2], DenseNet201 [4] and Xception [5] which are Convolutional Neural Networks [6] are analysed and compared and a novel model is built. We used the BraTS2020 [3] dataset which consists of images of four types Pituitary tumor, Meningioma tumor, Glioma tumor and no tumor we next performed resizing, re-scaling, preprocessing techniques on them. Following which we deployed the architecture to obtain our first set of results i.e, VGG19 gave accuracy of 70%, ResNet gave 72%, Xception gave 75% and DensetNet201 gave 76%. To make our accuracies better we performed image enhancement. [7]the process where adjusting of digital images takes place to ensure that the results are more suitable to display or for doing further image analysis is called image enhancement. Three types of Image enhancement techniques have been used: Adaptive Thresholding [8], Brightening [9], Contrasting [10] and we found that with contrasting we got the best results and VGG19 architecture gave highest accuracy of 83% proving the importance of image enhancement.

KLE Technological University Hubballi, India.

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Authors and Affiliations

  1. KLE Technological University Hubballi, Hubballi, India

    Anand Meti, Akanksha Rao & Pratyush Jha

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  1. Anand Meti
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  2. Akanksha Rao
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  3. Pratyush Jha
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Correspondence to Anand Meti .

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Editors and Affiliations

  1. Vardhaman College of Engineering, Hyderabad, India

    M. A. Jabbar

  2. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodríguez

  3. Autonomous University of Tamaulipas, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. University of Paris-Est Creteil, Vitry sur Seine, France

    Patrick Siarry

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Meti, A., Rao, A., Jha, P. (2023). Brain MRI Image Classification Using Deep Learning. In: Jabbar, M.A., Ortiz-Rodríguez, F., Tiwari, S., Siarry, P. (eds) Applied Machine Learning and Data Analytics. AMLDA 2022. Communications in Computer and Information Science, vol 1818. Springer, Cham. https://doi.org/10.1007/978-3-031-34222-6_7

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  • DOI: https://doi.org/10.1007/978-3-031-34222-6_7

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  • Online ISBN: 978-3-031-34222-6

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