Abstract
In recent years, deep learning is widely used in medical field for advance disease diagnosis. The purpose of this study is to analyze the effect of data pre-processing techniques on disease classification. The disease considered for the present work is brain tumor. The three different types of brain tumor are Glioma, Meningioma and Pituitary tumor. The motivation of this work is: the diagnosis of the brain tumor type at the early stage may lead to effective treatment. In image processing perspective, there are several methods which solves the disease classification problem. However, one of the recent popular deep learning algorithm known as, Convolutional Neural Networks (CNN) is mainly used for image classification tasks. The conventional CNN requires massive amount of annotated data, which is a challenge in the medical field. Capsulenet can overcome this drawback. Therefore, the present work uses the capsulenet for brain tumor classification. The proposed method shows that the data pre-processing plays a vital role in the improvement of the capsulenet architecture used for brain tumor classification.
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Vimal Kurup, R., Sowmya, V., Soman, K.P. (2020). Effect of Data Pre-processing on Brain Tumor Classification Using Capsulenet. In: Gunjan, V., Garcia Diaz, V., Cardona, M., Solanki, V., Sunitha, K. (eds) ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management. ICICCT 2019. Springer, Singapore. https://doi.org/10.1007/978-981-13-8461-5_13
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