Abstract
Multiple myeloma is a type of bone marrow cancer. Patient’s blood samples are analysed from protein gel strips and densitometer graphs which are then interpreted by a pathologist to diagnose multiple myeloma. This manual process of diagnosis is slow which is problematic as patients need to be diagnosed as soon as possible in order to prevent the condition from worsening, hence the need to automate this process. Given the success of machine learning in diagnosing diseases from images, this study investigates the use of machine learning approaches for the diagnosis of multiple myeloma. This is the first study investigating the automation of this process and hence presents a novel application of machine learning. The study compares machine learning approaches, artificial neural networks, convolutional neural networks, random forests and support vector machines, for the diagnosis of multiple myeloma from gel strips and densitometer graphs. The study has revealed that convolutional neural networks, specifically VGG16, is the most suitable approach for the detection of multiple myeloma.
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Acknowledgements
This work is based on the research supported wholly/in part by the National Research Foundation of South Africa (Grant Numbers 46712). Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the NRF. The authors also acknowledge the Sebia Group Cartridge for sponsoring the printing of the gel strips and graphs.
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Marimuthu, L., Pillay, N., Punchoo, R., Bhoora, S. (2021). A Comparison of Machine Learning Techniques for Diagnosing Multiple Myeloma. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_43
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