Kamireddy Rasool Reddy, Kandala Rajesh N V P S, Dhuli Ravindra, Polinati Srinivasu, Sonti Kamesh, Tadeusiewicz Ryszard, Pławiak Paweł
Department of ECE, NRI Institute of Technology (Autonomous), Vijayawada, India.
School of Electronics Engineering (SENSE), VIT-AP University, Amaravati, Andhra Pradesh, India.
PLoS One. 2024 Aug 1;19(8):e0306492. doi: 10.1371/journal.pone.0306492. eCollection 2024.
Brain tumor detection in clinical applications is a complex and challenging task due to the intricate structures of the human brain. Magnetic Resonance (MR) imaging is widely preferred for this purpose because of its ability to provide detailed images of soft brain tissues, including brain tissue, cerebrospinal fluid, and blood vessels. However, accurately detecting brain tumors from MR images remains an open problem for researchers due to the variations in tumor characteristics such as intensity, texture, size, shape, and location. To address these issues, we propose a method that combines multi-level thresholding and Convolutional Neural Networks (CNN). Initially, we enhance the contrast of brain MR images using intensity transformations, which highlight the infected regions in the images. Then, we use the suggested CNN architecture to classify the enhanced MR images into normal and abnormal categories. Finally, we employ multi-level thresholding based on Tsallis entropy (TE) and differential evolution (DE) to detect tumor region(s) from the abnormal images. To refine the results, we apply morphological operations to minimize distortions caused by thresholding. The proposed method is evaluated using the widely used Harvard Medical School (HMS) dataset, and the results demonstrate promising performance with 99.5% classification accuracy and 92.84% dice similarity coefficient. Our approach outperforms existing state-of-the-art methods in brain tumor detection and automated disease diagnosis from MR images.
在临床应用中，由于人类大脑结构复杂，脑肿瘤检测是一项复杂且具有挑战性的任务。磁共振（MR）成像因其能够提供包括脑组织、脑脊液和血管在内的软脑组织的详细图像，而被广泛用于此目的。然而，由于肿瘤特征（如强度、纹理、大小、形状和位置）的变化，从MR图像中准确检测脑肿瘤仍然是研究人员面临的一个未解决问题。为了解决这些问题，我们提出了一种结合多级阈值处理和卷积神经网络（CNN）的方法。首先，我们使用强度变换增强脑MR图像的对比度，突出图像中的感染区域。然后，我们使用建议的CNN架构将增强后的MR图像分类为正常和异常类别。最后，我们基于Tsallis熵（TE）和差分进化（DE）采用多级阈值处理，从异常图像中检测肿瘤区域。为了优化结果，我们应用形态学操作来最小化阈值处理引起的失真。使用广泛使用的哈佛医学院（HMS）数据集对所提出的方法进行评估，结果显示出有前景的性能，分类准确率为99.5%，骰子相似系数为92.84%。我们的方法在脑肿瘤检测和从MR图像进行自动疾病诊断方面优于现有的最先进方法。
