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Deep Learning Framework for the Prediction of Childhood Medulloblastoma

M. Muthalakshmi1,*, T. Merlin Inbamalar2, C. Chandravathi3, K. Saravanan4

1 Department of Biomedical Engineering, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
2 Department of Electronics and Instrumentation Engineering, Saveetha Engineering College, Chennai, Tamil Nadu, India
3 Department of Information Technology, J.J. College of Engineering and Technology, Trichy, Tamil Nadu, India
4 Department of Information Technology, R.M.D Engineering College, Kavaraipettai, Chennai, Tamil Nadu, India

* Corresponding Author: M. Muthalakshmi. Email:

Computer Systems Science and Engineering 2023, 46(1), 735-747.


This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling layers are used to classify histopathological images using a neural network classifier. The performance of the CMB classification system is evaluated using 1414 (10× magnification) and 1071 (100× magnification) augmented histopathological images with five classes of CMB such as desmoplastic, nodular, large cell, classic, and normal. Experimental results show that the average classification accuracy of 99.38% (10×) and 99.07% (100×) is attained by the proposed CNB classification system.


Cite This Article

M. Muthalakshmi, T. Merlin Inbamalar, C. Chandravathi and K. Saravanan, "Deep learning framework for the prediction of childhood medulloblastoma," Computer Systems Science and Engineering, vol. 46, no.1, pp. 735–747, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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