ENHANCED FILTERING METHODS FOR OPTIMAL BRAIN TUMOR SEGMENTATION AND CLASSIFICATION

Abstract

Brain tumor detection and classification remain pivotal challenges in medical imaging, particularly when using MRI scans for accurate diagnosis. This work surveys various advanced preprocessing, segmentation, and classification techniques that have been developed to enhance the accuracy and efficiency of brain tumor detection. The study emphasizes the critical role of preprocessing methods, such as surface feature extraction, thresholding, and morphological operations, in improving the quality of MRI images. Segmentation techniques, including hierarchical approaches, K-means clustering, and fuzzy C-means clustering, are explored for their effectiveness in delineating tumor boundaries. Additionally, the integration of supervised and unsupervised classification algorithms, including support vector machines (SVM), convolutional neural networks (CNN), and hybrid techniques, is examined for their ability to classify tumors accurately. The survey highlights recent advancements in neural network methodologies, such as Atrous Spatial Pyramid Pooling (ASPP) and Conditional Random Fields (CRF), which have significantly improved the precision and robustness of brain tumor segmentation and classification. This comprehensive review provides insights into the most effective strategies for enhancing brain tumor detection, offering a foundation for future research and clinical applications.

Brain tumor detection and classification remain pivotal challenges in medical imaging, particularly when using MRI scans for accurate diagnosis. This work surveys various advanced preprocessing, segmentation, and classification techniques that have been developed to enhance the accuracy and efficiency of brain tumor detection. The study emphasizes the critical role of preprocessing methods, such as surface feature extraction, thresholding, and morphological operations, in improving the quality of MRI images. Segmentation techniques, including hierarchical approaches, K-means clustering, and fuzzy C-means clustering, are explored for their effectiveness in delineating tumor boundaries. Additionally, the integration of supervised and unsupervised classification algorithms, including support vector machines (SVM), convolutional neural networks (CNN), and hybrid techniques, is examined for their ability to classify tumors accurately. The survey highlights recent advancements in neural network methodologies, such as Atrous Spatial Pyramid Pooling (ASPP) and Conditional Random Fields (CRF), which have significantly improved the precision and robustness of brain tumor segmentation and classification. This comprehensive review provides insights into the most effective strategies for enhancing brain tumor detection, offering a foundation for future research and clinical applications.