Abstract
Accurate diagnosis of brain diseases is a challenging task for the physicians. The manual detection of brain disorders is infeasible and tedious task. Magnetic resonance imaging (MRI) of brain images is one of the advanced neuroimaging techniques used in recent days. The research reveals that the machine learning techniques have significant contributions for the classification of brain MRI images. Extreme learning machine (ELM) has been used in image classification. In this paper, an attempt has been made to hybridize the convolutional neural network (CNN) model. The proposed CNN-ELM model integrates the CNN model for feature engineering and the ELM method for classification of brain MRI images. For simulation study, brain tumor MRI image dataset is considered and the results suggest that the proposed CNN-ELM model outperformed the CNN model in classifying the brain images in the dataset.
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Satapathy, P., Pradhan, S.K., Hota, S., Mahakud, R.R. (2021). Brain Image Classification Using the Hybrid CNN Architecture. In: Das, S., Mohanty, M.N. (eds) Advances in Intelligent Computing and Communication. Lecture Notes in Networks and Systems, vol 202. Springer, Singapore. https://doi.org/10.1007/978-981-16-0695-3_32
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DOI: https://doi.org/10.1007/978-981-16-0695-3_32
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