A Hybrid Deep Learning-Based Approach for Brain Tumor Classification
University of Engineering and Technology Taxila · University of Science and Technology Bannu · +8 more institutions
Abstract
Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of people die due to deadly brain tumors. Therefore, accurate detection and classification are essential in the treatment of brain tumors. Numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand-crafted features, which is very time-consuming. On the contrary, DL is very robust in feature extraction and has recently been widely used for classification and detection purposes. Therefore, in this work, we propose a hybrid deep learning model called DeepTumorNet for three types of…
Citation impact
- FWCI
- 25.39
- Percentile
- 100%
- References
- 59
Authors
9- ARAsaf Raza
University of Engineering and Technology Taxila
- HAHuma AyubCorresponding
University of Engineering and Technology Taxila
- JAJaved Ali Khan
University of Science and Technology Bannu
- IAIjaz AhmadCorresponding
Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences
- ASAhmed S. Salama
Future University in Egypt
Topics & keywords
- Convolutional neural network
- Artificial intelligence
- Deep learning
- Computer science
- F1 score
- Feature extraction
- Pattern recognition (psychology)
- Feature (linguistics)