Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network
| Autor: | Sirajul Huque, R. Ganesh Kumar, A. Vasantharaj, Pacha Shoba Rani, Sebahadin Nasir Shafi, K. S. Raghuram |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Deep learning Brain tumor Swarm behaviour Capsule Magnetic resonance imaging Image segmentation medicine.disease Computer Graphics and Computer-Aided Design Computer Science Applications Neuroimaging medicine Medical imaging Computer Vision and Pattern Recognition Artificial intelligence business Biomedical engineering |
| Zdroj: | International Journal of Image and Graphics. :2240001 |
| ISSN: | 1793-6756 0219-4678 |
| Popis: | Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|