Microscopic brain tumor detection and classification using 3D CNN and feature selection architecture.
Amjad RehmanMuhammad Attique KhanTanzila SabaZahid MehmoodUsman TariqeNoor AyeshaPublished in: Microscopy research and technique (2020)
Brain tumor is one of the most dreadful natures of cancer and caused a huge number of deaths among kids and adults from the past few years. According to WHO standard, the 700,000 humans are being with a brain tumor and around 86,000 are diagnosed since 2019. While the total number of deaths due to brain tumors is 16,830 since 2019 and the average survival rate is 35%. Therefore, automated techniques are needed to grade brain tumors precisely from MRI scans. In this work, a new deep learning-based method is proposed for microscopic brain tumor detection and tumor type classification. A 3D convolutional neural network (CNN) architecture is designed at the first step to extract brain tumor and extracted tumors are passed to a pretrained CNN model for feature extraction. The extracted features are transferred to the correlation-based selection method and as the output, the best features are selected. These selected features are validated through feed-forward neural network for final classification. Three BraTS datasets 2015, 2017, and 2018 are utilized for experiments, validation, and accomplished an accuracy of 98.32, 96.97, and 92.67%, respectively. A comparison with existing techniques shows the proposed design yields comparable accuracy.
Keyphrases
- deep learning
- convolutional neural network
- neural network
- artificial intelligence
- machine learning
- loop mediated isothermal amplification
- computed tomography
- contrast enhanced
- magnetic resonance imaging
- label free
- oxidative stress
- real time pcr
- papillary thyroid
- magnetic resonance
- squamous cell
- rna seq
- free survival
- quantum dots
- dual energy