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CNN-Based Brain Tumor Detection Model Using Local Binary Pattern and Multilayered SVM Classifier
Morarjee Kolla1, Rupesh Kumar Mishra1, S Zahoor Ul Huq2
1Department of Computer Science and Engineering, Chaitanya Bharthi Institute of Technology, Hyderabad, Telangana, India.
Computational Intelligence and Neuroscience
|July 7, 2022
Summary
This study introduces an automated brain tumor detection system using convolutional neural networks (CNN) and multilayered support vector machines (ML-SVM). The model enhances diagnostic accuracy and efficiency for physicians.
Area of Science:
- Medical Imaging and Artificial Intelligence
- Computational Neuroscience
- Machine Learning in Healthcare
Background:
- Accurate brain tumor detection and classification are crucial for effective patient treatment and require efficient, automated systems to reduce manual workload.
- Existing methods may lack the precision and speed needed for timely diagnosis, highlighting the need for advanced computational approaches.
Purpose of the Study:
- To develop and evaluate an autonomous model for brain tumor segmentation and detection using advanced deep learning techniques.
- To integrate Local Binary Pattern (LBP) and Multilayered Support Vector Machine (ML-SVM) with Convolutional Neural Networks (CNN) for enhanced performance.
Main Methods:
- Image preprocessing involved filtering and intensity normalization, followed by patch extraction.
- Feature extraction utilized grayscale conversion and Local Binary Pattern (LBP) on RGB images.
- A Convolutional Neural Network (CNN) was employed for feature extraction, and a Multilayered Support Vector Machine (ML-SVM) for object detection and classification.
Main Results:
- The proposed model demonstrated effectiveness in determining the presence or absence of brain tumors.
- Performance was evaluated against existing methods using metrics including Dice Similarity Coefficient (DSC), Jaccard Similarity Index (JSI), sensitivity, accuracy, specificity, and precision.
Conclusions:
- The developed autonomous system offers a promising approach for brain tumor diagnosis, integrating CNN and ML-SVM for robust detection and segmentation.
- This intelligent system has the potential to significantly aid physicians by automating complex diagnostic tasks, improving efficiency and accuracy in clinical practice.
