Brain tumor detection using fusion of hand crafted and deep learning features

By Tanzila Saba, A. S. Mohamed, M. El-Affendi, Javeria Amin, Muhammad Sharif. Cognitive Systems Research, 2020

371 citations recorded by the source corpus.

Abstract The perilous disease in the worldwide now a days is brain tumor. Tumor affects the brain by damaging healthy tissues or intensifying intra cranial pressure. Hence, rapid growth in tumor cells may lead to death. Therefore, early brain tumor diagnosis is a more momentous task that can save patient from adverse effects. In the proposed work, the Grab cut method is applied for accurate segmentation of actual lesion symptoms while Transfer learning model visual geometry group (VGG-19) is fine-tuned to acquire the features which are then concatenated with hand crafted (shape and texture) features through serial based method. These features are optimized through entropy for accurate and fast classification and fused vector is supplied to classifiers. The presented model is tested on top medical image computing and computer-assisted intervention (MICCAI) challenge databases including multimodal brain tumor segmentation (BRATS) 2015, 2016, and 2017 respectively. The testing results with dice similarity coefficient (DSC) achieve 0.99 on BRATS 2015, 1.00 on BRATS 2015 and 0.99 on BRATS 2017 respectively.

Subfields: Computer Vision and Pattern Recognition, Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3fd5027b3f844f173c3423aae275abfad5c494ca

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yudong Zhang
2. Jian Wang
3. Ceren Kaya
4. Shui-hua Wang
5. Hengde Zhu
6. Ali Narin
7. Ziynet Pamuk
8. M. Ahsan

In Radiology, Nuclear Medicine and Imaging

1. Ceren Kaya
2. Ali Narin
3. Ziynet Pamuk
4. M. Ahsan
5. Zahed Siddique
6. Mohammad Belayet Hossain
7. Chiranjibi Sitaula
8. Youness Chawki

Citations in the corpus

• MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers (2021, neutral): “…feature extraction methods for MRI-based brain tumor classification using pre-trained deep CNN models: ResNet-50 [69,70], ResNet-101 [71], DenseNet-121 [70,72], VGG-16 [69,70], VGG-19 [70,73], AlexNet [74], Inception V1 (GoogLeNet) [29], Inception V3 [69,75], and MobileNet V2 [76].”
• Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “As a result of these analyzes, detection and diagnosis of diseases such as diabetes mellitus, brain tumor, skin cancer and breast cancer are provided with convenience [36-41].”
• A Review of Deep Learning on Medical Image Analysis (2020, methodology): “[42] utilized both transfer learning model based on VGG-19 and handcrafted features by serial method and claimed to obtain quite good performance on several top brain image challenge databases.”