MVPO Predictor: Deep Learning-Based Tumor Classification and Survival Prediction of Brain Tumor Patients with MRI Using Multi-Verse Political Optimizer
| Autor: | R. Rajeswari, G. Neelima, Balajee Maram, Anupama Angadi |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | International Journal of Pattern Recognition and Artificial Intelligence. 36 |
| ISSN: | 1793-6381 0218-0014 |
| DOI: | 10.1142/s0218001422520061 |
| Popis: | Brain tumor is a severe nervous disorder that causes damage to health and often leads to death. Therefore, it is significant to classify the brain tumor at an early stage as it increases the survival rate of patients. One of the commonly employed imaging modalities for brain tumor classification is Magnetic Resonance Imaging (MRI). However, it is relatively complex to perform the brain tumor classification process due to the variations of type, shape, size and tumor location. To overcome such issues and classify the tumor more accurately, a deep learning classifier named Deep Maxout network is developed to classify the tumor into different grades. Based on the classification result, the features connected with the tumor grades are effectively acquired to make the survival prediction process. Deep learning is an effective and robust classifier model employed to perform the tumor classification or detection process with the MRI modality. Here, the survival prediction of tumor patients is carried out by the Deep Long Short-Term Memory (LSTM) classifier. Accordingly, the proposed method achieved higher performance using accuracy, sensitivity, specificity and prediction error with the values of 0.9434, 0.9324, 0.9202 and 0.0579. |
| Databáze: | OpenAIRE |
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