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Deep Learning Based Lightweight Model for Brain Tumor Classification and Segmentation
Andleeb, Ifrah Hussain, B. Zahid Ansari, Salik Ansari, Mohammad Samar Kanwal, Nadia Aslam, Asra
Andleeb, Ifrah
Hussain, B. Zahid
Ansari, Salik
Ansari, Mohammad Samar
Kanwal, Nadia
Aslam, Asra
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Aligarh Muslim University; University of Chester; Keele University; University of Leeds
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2024-02-01
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Abstract
This paper presents two lightweight deep learning models for efficient detection and segmentation of brain tumors from MRI scans. A custom-made Convolutional Neural Network (CNN) is designed for identification of four different classes of brain tumors viz. Meningioma, Glioma, Pituitary brain tumor and normal (no tumor). Furthermore, another tailor-made lightweight model is presented for the segmentation of the tumor from the Magnetic Resonance Imaging (MRI) scans. The output of the segmentation model is the ‘mask’ depicting the tumor region. The overall performance in terms of detection accuracy, and segmentation accuracy, for the two models is found to be approximately 95% for both the cases individually. The proposed models are worthy additions to the existing literature on brain tumor classification and segmentation models due to their low-parameter count which make the models amenable for deployment on resource-constrained edge hardware.
Citation
Andleeb, I., Hussain, B. Z., Ansari, S., Ansari, M. S., Kanwal, N., & Aslam, A. (2024). Deep learning based lightweight model for brain tumor classification and segmentation. In N. Naik, P. Jenkins, P. Grace, L. Yang, & S. Prajapat (Eds.) Advances in Computational Intelligence Systems (pp. 491–503). Springer. https://doi.org/10.1007/978-3-031-47508-5_38
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Springer
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Conference Contribution
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This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-47508-5_38]. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms.
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Advances in Intelligent Systems and Computing; 1453
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9783031475078