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Case Studies | Computer Science & Engineering | India | Volume 13 Issue 12, December 2024 | Popularity: 5.3 / 10

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A Deep Learning Algorithm Improvement for Brain Tumour Segmentation Using Kernel-Based CNN and M-SVM

Nishant Kumar Singh, Dr. Pushpneel Verma

Abstract: Brain tumor detection and segmentation are crucial tasks in medical image analysis, offering significant implications for early diagnosis and treatment. In this paper, we propose an enhanced deep learning approach combining Kernel-based Convolutional Neural Networks (K-CNN) and Multi-class Support Vector Machines (M-SVM) to improve the accuracy and efficiency of brain tumor identification. This hybrid model leverages the strengths of both methods: the feature extraction capabilities of K-CNN and the classification prowess of M-SVM. The experimental results demonstrate improved performance metrics compared to existing methodologies. Brain tumor segmentation is a critical step in the diagnosis and treatment planning of brain disorders, particularly for malignant and benign tumors. Accurate segmentation from MRI images poses significant challenges due to the complex and heterogeneous nature of brain tumors. This paper presents an enhanced deep learning framework that combines a kernel-based convolutional neural network (CNN) with a multi-class support vector machine (M-SVM) to improve the accuracy and robustness of brain tumor segmentation. The kernel-based CNN is designed to efficiently extract high-dimensional features, while the M-SVM classifier refines the segmentation by addressing class imbalances and overlapping boundaries. Extensive experiments on benchmark MRI datasets demonstrate the proposed method?s superiority over traditional approaches, achieving improved segmentation precision and reduced computational overhead. The results indicate the potential of the proposed hybrid model to advance clinical applications in brain tumor analysis.

Keywords: Magnetic resonance image, Brain tumor segmentation, Deep learning

Edition: Volume 13 Issue 12, December 2024

Pages: 1536 - 1542

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