Bone tumors are one of the most common diseases in bone and soft tissue tumors, and accurate classification is crucial for developing effective treatment strategies. However, traditional pathological morphology diagnosis is subjective and uncertain, requiring highly specialized knowledge and experience. Therefore, how to efficiently and accurately classify bone tumor types based on pathological images is an urgent problem to be solved. In this study, a lightweight convolutional neural network (CNN) model called Efficient and Enhance Network (EENet) was proposed for the automatic recognition of bone tumor pathological images. The model introduced efficient channel attention (ECA) blocks to improve the efficiency of feature extraction and reduce computational complexity. A comprehensive dataset of bone pathological images, including five types of bone lesion tissues and normal bone tissue, was compiled for the research. Due to the low incidence of bone tumors and the difficulty in obtaining a large amount of data, the transfer learning method was chosen to overcome the problem of limited data volume. The experimental results from the fivefold cross‐validation demonstrate that the proposed model achieves 99.06% accuracy, 99.08% precision, 99.19% recall, 99.81% specificity, and 99.03% F1‐score in the bone tumor classification task, highlighting its potential as a clinical diagnostic tool.

中文翻译：

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EENet：基于卷积神经网络的深度学习方法在骨肿瘤病理诊断中的应用

骨肿瘤是骨和软组织肿瘤中最常见的疾病之一，准确的分类对于制定有效的治疗策略至关重要。然而，传统的病理形态学诊断具有主观性和不确定性，需要高度专业的知识和经验。因此，如何根据病理图像高效、准确地对骨肿瘤类型进行分类是亟待解决的问题。在这项研究中，提出了一种称为高效增强网络（EENet）的轻量级卷积神经网络（CNN）模型，用于骨肿瘤病理图像的自动识别。该模型引入了高效的通道注意（ECA）块来提高特征提取的效率并降低计算复杂度。该研究编制了完整的骨病理图像数据集，包括五种骨病变组织和正常骨组织。由于骨肿瘤发病率较低，且难以获取大量数据，因此选择迁移学习方法来克服数据量有限的问题。五重交叉验证的实验结果表明，所提出的模型在骨肿瘤分类任务中实现了 99.06% 的准确率、99.08% 的精确度、99.19% 的召回率、99.81% 的特异性和 99.03% 的 F1 分数，凸显了其作为临床分类任务的潜力。诊断工具。