Enhanced brain tumor classification using an optimized multi-layered convolutional neural network architecture
| Autor: | Ali Ashkanani, Sa'ed Abed, Mohammad Al-Shayeji, Jassim Al-Buloushi |
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| Rok vydání: | 2021 |
| Předmět: |
Hyperparameter
Computer Networks and Communications business.industry Computer science Multitier architecture Deep learning Bayesian optimization Process (computing) 020207 software engineering Pattern recognition 02 engineering and technology Convolutional neural network Field (computer science) ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture 0202 electrical engineering electronic engineering information engineering Media Technology Artificial intelligence Architecture business Software |
| Zdroj: | Multimedia Tools and Applications. 80:28897-28917 |
| ISSN: | 1573-7721 1380-7501 |
| Popis: | Detecting and classifying a brain tumor is a challenge that consumes a radiologist’s time and effort while requiring professional expertise. To resolve this, deep learning techniques can be used to help automate the process. The aim of this paper is to enhance the accuracy of brain tumor classification using a new layered architecture of deep neural networks rather than the current state-of-the-art algorithms. In this paper, we propose automated tumor classification by concatenating two convolutional neural network structures of layers and tuning the hyperparameters by utilizing Bayesian optimization. The proposed solution focuses on enhancing the accuracy of classifying tumors to increase the level of trust in the technologies employed in the medical field. The work is tested and evaluated to predict the classification of magnetic resonance imaging inputs and achieving a higher accuracy (97.37%) than other similar works, with accuracies between 84.19% and 96.13%, for the same dataset. |
| Databáze: | OpenAIRE |
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