Advanced Brain Tumor Diagnosis using Haralick Texture Features and Multiclass SVM in MRI Imaging-Reference-Cited by-同舟云学术

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Advanced Brain Tumor Diagnosis using Haralick Texture Features and Multiclass SVM in MRI Imaging

Published:2024-05-16 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR)
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Author:

Karimullah Shaik¹,Basha Mohammed Mahaboob²,Shaik Fahimuddin¹,Gundala Srinivasulu³,Stan Anca⁴,Stan Ovidiu Petru⁵

Affiliation:

1. AITS,Departmrnt of ECE,Rajampet,Andhra Pradesh,India

2. Sreenidhi Institute of Science and Technology,Department of ECE,India

3. Lakireddy Bali Reddy College of Engineering,Department of ECE,Mylavaram,Andhra Pradesh

4. Technical University of Cluj Napoca,Industrial Engineering, Robotics and Production Management,Romania

5. Technical University of Cluj Napoca,Department of Automation, Automation and Computer Science,Romania

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10554066/10554069/10554101.pdf?arnumber=10554101

Reference12 articles.

1. 3D variational brain tumor segmentation using Dirichlet priors on a clustered feature set

2. Brain MRI Slices Classification Using Least Squares Support Vector Machine

3. Convolutional Neural Network based Image Classification and Detection of Abnormalities in MRI Brain Images

4. Brain tumor classification in MRI image using convolutional neural network

5. Application of deep transfer learning for automated brain abnormality classification using MR images

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