Abstract
The article describes the effect of data augmentation on classification systems that are used to differentiate abnormalities in medical images. The imbalance in data leads to bias in classifying the various states. Medical images, in general, are deficit of data and thus augmentation will provide an enriched dataset for the learning systems to identify and differentiate between deformities. We have explored additional data generation by applying affine transformations and the instance based transformation that could result in improving the classification accuracy. We perform experiments on the segmented dataset of vertebral bodies from MR images, by augmenting and classified the same, using Naive Bayes, Radial Basis Function and Random Forest methods. The performance of classifiers was evaluated using the True Positive Rate (TPR) obtained at various thresholds from the ROC curve and the area under ROC curve. For the said application, Random Forest method is found to provide a stable TPR with the augmented dataset compared to the raw dataset.
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Acknowledgments
The first author would like to thank the Department of Science and Technology, India, for supporting the research through INSPIRE fellowship. The authors would like to thank Apollo Speciality Hospitals for providing images and Dr. G. Jayaraj, Senior Consultant, Dept of Radiology and Imaging Sciences, Apollo Speciality Hospitals, Chennai, for his valuable inputs.
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Athertya, J.S., Kumar, G.S. (2018). Data Augmentation Techniques for Classifying Vertebral Bodies from MR Images. In: R, S., Sharma, M. (eds) Data Science Analytics and Applications. DaSAA 2017. Communications in Computer and Information Science, vol 804. Springer, Singapore. https://doi.org/10.1007/978-981-10-8603-8_4
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DOI: https://doi.org/10.1007/978-981-10-8603-8_4
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