Development of automatic glioma brain tumor detection system using deep convolutional neural networks |
| |
| Authors: | Thiruvenkadam Kalaiselvi Thiyagarajan Padmapriya Padmanaban Sriramakrishnan Venugopal Priyadharshini |
| |
| Affiliation: | 1. Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India;2. Department of Computer Applications, Kalasalingam Academy of Research and Education (Deemed to be University), Krishnankoil, Tamil Nadu, India;3. Bachelor of Medicine and Bachelor of Surgery (MBBS), Government Medical College, Omandurar Government Estate, Chennai, Tamil Nadu, India |
| |
| Abstract: | We have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high-grade glioma and low-grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state-of-art methods. |
| |
| Keywords: | BraTS deep learning glioma tumor neural networks tumor detection WBA |
|
|
