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Publication Type : Conference Paper
Publisher : Springer Singapore
Source : ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, Springer Singapore, Singapore (2020)
ISBN : 9789811384615
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Electronics and Communication
Year : 2020
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.
Cite this Research Publication : V. R. Kurup, Sowmya, V., and Dr. Soman K. P., “Effect of Data Pre-processing on Brain Tumor Classification Using Capsulenet”, in ICICCT 2019 – System Reliability, Quality Control, Safety, Maintenance and Management, Singapore, 2020.