Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.
Sci Rep. 2024 Feb 26;14(1):4584. doi: 10.1038/s41598-024-54864-6.
Gliomas are primary brain tumors caused by glial cells. These cancers' classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.
神经胶质瘤是由神经胶质细胞引起的原发性脑肿瘤。这些癌症的分类和分级对于预后和治疗计划至关重要。深度学习（DL）有可能改进脑肿瘤的数字病理学研究。在本文中，我们开发了一种在组织病理学图像上可视化预测性肿瘤分级模型的技术，通过强调预测中的特征和异质性来帮助指导医生。所提出的技术是基于 YOLOv5 和 ResNet50 的混合模型。YOLOv5 的功能是在大的组织病理学全切片图像（WSI）中定位和分类肿瘤。所提出的技术将 ResNet 纳入 YOLOv5 框架的特征提取中，检测结果表明我们的混合网络对于从组织病理学图像中识别脑肿瘤是有效的。接下来，我们使用极端梯度提升分类器估计胶质瘤的等级。该分类器很好地处理了组织病理学图像中存在的高维特征和非线性相互作用。DL 技术已在前瞻性计算机辅助诊断系统中用于脑肿瘤诊断。然而，通过将 YOLOv5 和 ResNet50 架构结合到一个专门设计用于在组织病理学 WSI 中进行准确肿瘤定位和预测分级的混合模型中，我们的研究提出了一种新的方法，推动了该领域的发展。通过利用两个模型的优势，这种创造性的集成超越了传统技术，产生了改进的肿瘤定位准确性和全面的特征提取。此外，通过将 ResNet50 集成到 YOLOv5 框架中，我们的方法解决了梯度爆炸的问题，确保了稳定的训练动态和强大的模型性能。所提出的技术使用癌症基因组图谱数据集进行测试。在实验中，我们的模型在同一数据集上优于其他标准方法。我们的结果表明，所提出的混合模型在低级别胶质瘤（LGG）II 和 LGG III 之间的肿瘤亚型鉴别方面具有显著影响。该模型在分类四个等级方面表现良好，准确率为 97.2%，精度为 97.8%，灵敏度为 98.6%，Dice 相似系数为 97%。这些结果优于当前用于从高级别胶质瘤中识别 LGG 的方法，并在文献中分类四类胶质瘤方面提供了有竞争力的性能。
