Abstract
Childhood medulloblastoma (MB) is the most common embryo brain tumor and an area that needs utmost attention, as clinical diagnosis can be very difficult in case of infants and children. The rate of survival can increase with prompt diagnosis. Till date, there is no computer aided methodology for identification of childhood medulloblastoma and its subtypes. The diagnosis depends on qualitative visual inspection of the histological slides of the biopsy samples by clinical experts. We convert this qualitative judgment to quantitative features after digitization of the images. The feature set obtained from digital analysis from these biopsy tissues is very large and is computationally expensive. In this study, we examine whether the features are statistically significant for analysis towards classifying childhood MB from normal samples and its various subtypes, using MANOVA. Further, this technique is used as a feature reduction technique, which proves that it can be effectively used as such. Infact, the simplicity of the technique makes it a better choice when considering a sizeably high number of features.
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Acknowledgements
We would like to convey our sincere thanks to Dr. Basanta Kr. Baishya, Head of the department of neurosurgery, Guwahati Medical College, and Dr. Inamul Haque, MCH Trainee from the Department of Neurosurgery, Guwahati Medical College for providing us the tissue blocks and Dr. Anup Das from Ayursundra Healthcare Pvt. Ltd. For processing the slides. We would further thank Dr. Shabnam Ahmed of Guwahati Neurological Research Centre, Sixmile for dedicating her time and effort and helping us in image acquisition and providing the ground truth. We are grateful to Institute of Advanced Study in Science and Technology (IASST), Guwahati for giving us the platform to perform our research.
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D.D wrote the paper and performed the experiments. L.B.M supervised the whole idea and write up. S.A. and B.K.B was the medical guidance throughout the study.
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Das, D., Mahanta, L.B., Ahmed, S. et al. A study on MANOVA as an effective feature reduction technique in classification of childhood medulloblastoma and its subtypes. Netw Model Anal Health Inform Bioinforma 9, 16 (2020). https://doi.org/10.1007/s13721-020-0221-5
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DOI: https://doi.org/10.1007/s13721-020-0221-5