Name
Abstract | ||
|---|---|---|
Magnetic Resonance Imaging(MRI) is one of the commonly used medical imaging modality that provides informative data for brain tumor diagnosis other than Computed Tomography(CT). A key challenge when a physician studies the MRI data is the time and effort he has to put in diagnosing the tumors. The objective of this research is to recognize the tumor type when a collection of MRI images of a patient is given. To achieve this goal, a deep learning algorithm is developed using Convolutional Neural Networks(CNNs). Nowadays, most of image classification problems use CNNs as they deliver higher precision and accuracy compared to other existing algorithms. Here, a sophisticated CNN model is developed, trained using cross validation and tested on brain MRI images obtained from open databases. The performance of the proposed model is promising. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ICIINFS.2017.8300364 | 2017 IEEE International Conference on Industrial and Information Systems (ICIIS) |
Keywords | DocType | ISSN |
MRI,Brain tumor,Convolutional Neural Networks,Cross Validation | Conference | 2164-7011 |
ISBN | Citations | PageRank |
978-1-5386-1677-2 | 0 | 0.34 |
References | Authors | |
3 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Narmada M. Balasooriya | 1 | 0 | 0.34 |
| Ruwan D. Nawarathna | 2 | 27 | 3.10 |