In medical imaging, segmenting brain tumor becomes a vital task, and it provides a way for early diagnosis and treatment. Manual segmentation of brain tumor in magnetic resonance (MR) images is a time‐consuming and challenging task. Hence, there is a need for a computer‐aided brain tumor segmentation approach. Using deep learning algorithms, a robust brain tumor segmentation approach is implemented by integrating convolution neural network (CNN) and multiple kernel K means clustering (MKKMC). In this proposed CNN‐MKKMC approach, classification of MR images into normal and abnormal is performed by CNN algorithm. At next, MKKMC algorithm is employed to segment the brain tumor from the abnormal brain image. The proposed CNN‐MKKMC algorithm is evaluated both visually and objectively in terms of accuracy, sensitivity, and specificity with the existing segmentation methods. The experimental results demonstrate that the proposed CNN‐MKKMC approach yields better accuracy in segmenting brain tumor with less time cost.

中文翻译：

---

集成卷积神经网络和多核K均值聚类的深度学习模型，用于在磁共振图像中分割脑肿瘤

在医学成像中，分割脑肿瘤成为一项至关重要的任务，它为早期诊断和治疗提供了一种途径。在磁共振（MR）图像中手动分割脑肿瘤是一项耗时且具有挑战性的任务。因此，需要一种计算机辅助的脑肿瘤分割方法。使用深度学习算法，通过集成卷积神经网络（CNN）和多核K均值聚类（MKKMC）来实现鲁棒的脑肿瘤分割方法。在此CNN‐MKKMC方法中，通过CNN算法将MR图像分为正常图像和异常图像。接下来，采用MKKMC算法从异常的大脑图像中分割出脑肿瘤。所提出的CNN‐MKKMC算法在准确性，灵敏度，和现有分割方法的特异性。实验结果表明，所提出的CNN‐MKKMC方法在分割脑肿瘤方面具有更高的准确性，且所需时间更少。