Y-net: a reducing gaussian noise convolutional neural network for MRI brain tumor classification with NADE concatenation
| Autor: | Maryam Kheirabadi, Seyyed Javad Seyyed Mahdavi, Raheleh Hashemzehi, Seyed Reza Kamel |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Artificial neural network Brain Neoplasms business.industry Computer science Concatenation Neuroimaging Magnetic resonance imaging Pattern recognition White noise Net (mathematics) Magnetic Resonance Imaging Convolutional neural network symbols.namesake Gaussian noise Quality of Life medicine symbols Humans Neural Networks Computer Mri brain Artificial intelligence business General Nursing |
| Zdroj: | Biomedical Physics & Engineering Express. 7:055006 |
| ISSN: | 2057-1976 |
| DOI: | 10.1088/2057-1976/ac107b |
| Popis: | Brain tumors are among the most serious cancers that can have a negative impact on a person's quality of life. The magnetic resonance imaging (MRI) analysis detects abnormal cell growth in the skull. Recently, machine learning models such as artificial neural networks have been used to detect brain tumors more quickly. To classify brain tumors, this research introduces the Y-net, a new convolutional neural network (CNN) based on the convolutional U-net architecture. We apply a NADE concatenation method in pre-processing the MR images for enhanced Y-net performance. We put our approach to the test using two MRI datasets of brain tumors. The first dataset contains three different types of brain tumors, while the second dataset includes a separate category for healthy brains. We show that our model is resistant to white noise and can obtain excellent classification accuracy with a limited number of medical images. |
| Databáze: | OpenAIRE |
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