Technol Health Care. 2024;32(6):4239-4256. doi: 10.3233/THC-240052.
Brain tumor is an extremely dangerous disease with a very high mortality rate worldwide. Detecting brain tumors accurately is crucial due to the varying appearance of tumor cells and the dimensional irregularities in their growth. This poses a significant challenge for detection algorithms. Currently, there are numerous algorithms utilized for this purpose, ranging from transform-based methods to those rooted in machine learning techniques. These algorithms aim to enhance the accuracy of detection despite the complexities involved in identifying brain tumor cells. The major limitation of these algorithms is the mapping of extracted features of a brain tumor in the classification algorithms.
To employ a combination of transform methods to extract texture feature from brain tumor images.
This paper employs a combination of transform methods based on sub band decomposition for texture feature extraction from MRI scans, hybrid feature optimization methods using firefly and glow-worm algorithms for selection of feature, employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods.
The algorithm under consideration has been put into practice using MATLAB, utilizing datasets from BRATS (Brain Tumor Segmentation) for the years 2013, 2015, and 2018. These datasets serve as the foundation for testing and validating the algorithm's performance across different time periods, providing a comprehensive assessment of its effectiveness in detecting brain tumors. The proposed algorithm achieves maximum detection accuracy, detection sensitivity and specificity up to 98%, 99% and 99.5% respectively. The experimental outcomes showcase the efficiency of the algorithm in detection of brain tumor.
The proposed work mainly contributes in brain tumor detection in the following aspects: a) use of combination of transform methods for texture feature extraction from MRI scans b) hybrid feature selection methods using firefly and glow-worm optimization algorithms for selection of feature c) employment of MKSVM algorithm and stacking ensemble classifier for classification and application of the feature of fusion of different feature extraction methods.
脑肿瘤是一种极其危险的疾病，在全球范围内死亡率非常高。由于肿瘤细胞的形态各异，生长维度不规则，因此准确检测脑肿瘤至关重要。这给检测算法带来了很大的挑战。目前，有许多算法可用于此目的，从基于变换的方法到基于机器学习技术的方法都有。这些算法旨在提高检测的准确性，尽管识别脑肿瘤细胞涉及到很多复杂的问题。这些算法的主要局限性在于在分类算法中对脑肿瘤提取特征的映射。
采用变换方法组合从 MRI 扫描中提取脑肿瘤的纹理特征。
本文采用基于子带分解的变换方法组合，从 MRI 扫描中提取纹理特征，使用萤火虫和夜光虫算法的混合特征优化方法选择特征，使用 MKSVM 算法和堆叠集成分类器进行分类，并应用不同特征提取方法的特征融合。
该算法使用 MATLAB 实现，使用 BRATS（脑肿瘤分割）数据集进行 2013 年、2015 年和 2018 年的测试。这些数据集为测试和验证算法在不同时间段的性能提供了基础，全面评估了其在检测脑肿瘤方面的有效性。所提出的算法的最大检测准确率、检测灵敏度和特异性分别达到 98%、99%和 99.5%。实验结果展示了算法在脑肿瘤检测中的效率。
所提出的工作主要在以下几个方面对脑肿瘤检测做出了贡献：a）使用变换方法组合从 MRI 扫描中提取纹理特征；b）使用萤火虫和夜光虫优化算法的混合特征选择方法选择特征；c）使用 MKSVM 算法和堆叠集成分类器进行分类，并应用不同特征提取方法的特征融合。
