Liu Qian, She Xing, Xia Qian
Institute of Arts & Design, Shandong Women's University, Jinan, PR China.
School of Arts and Design, Anhui University of Technology, Ma'anshan, PR China.
J Bone Oncol. 2024 Nov 4;49:100644. doi: 10.1016/j.jbo.2024.100644. eCollection 2024 Dec.
The incidence of osteosarcoma (OS) is low, but primary malignant bone tumors rank third among the causes of death in cancer patients under the age of 20. Currently, analysis of cellular structure and tumor morphology through microscopic images remains one of the main diagnostic methods for osteosarcoma. However, this completely manual approach is tedious, time-consuming, and difficult to diagnose accurately due to the similarities in certain characteristics of malignant and benign tumors.
Leveraging the potential of artificial intelligence (AI) in assessing and classifying images, this study explored a modified CA-MobileNet V3 model that was embedded into innovative microscope products to enhance the microscope's feature extraction capabilities and help reduce misclassification during diagnosis.
The intelligent recognition model method introduced in this paper has significant advantages in retrieval and classification of osteosarcoma cells and other cell types. Compared with models such as ShuffleNet V2, EfficientNet V2, Mobilenet V3 (without transfer learning), TL-MobileNet V3 (with transfer learning), etc., the model size is only 5.33 MB, is a lightweight model, and the accuracy of the improved model reached 98.69 %. In addition, the artificial intelligence microscope (AIM) with integrated design based on this model can also help improve diagnostic efficiency.
The innovative method of the CA-MobileNet V3 automatic classification model based on deep learning provides an efficient and reliable solution for the pathological diagnosis of osteosarcoma. This study contributes to medical image analysis and provides doctors with an accurate and valuable tool for microscopic diagnosis. It also promotes the advancement of artificial intelligence in medical imaging technology.
骨肉瘤（OS）的发病率较低，但原发性恶性骨肿瘤是20岁以下癌症患者死亡原因中的第三位。目前，通过显微镜图像分析细胞结构和肿瘤形态仍然是骨肉瘤的主要诊断方法之一。然而，这种完全手动的方法既繁琐又耗时，而且由于恶性肿瘤和良性肿瘤某些特征的相似性，难以准确诊断。
本研究利用人工智能（AI）在图像评估和分类方面的潜力，探索了一种改进的CA-MobileNet V3模型，该模型被嵌入到创新型显微镜产品中，以增强显微镜的特征提取能力，并有助于减少诊断过程中的错误分类。
本文引入的智能识别模型方法在骨肉瘤细胞及其他细胞类型的检索和分类方面具有显著优势。与ShuffleNet V2、EfficientNet V2、Mobilenet V3（无迁移学习）、TL-MobileNet V3（有迁移学习）等模型相比，该模型大小仅为5.33MB，是一个轻量级模型，改进后的模型准确率达到了98.69%。此外，基于该模型的一体化设计的人工智能显微镜（AIM）也有助于提高诊断效率。
基于深度学习的CA-MobileNet V3自动分类模型的创新方法为骨肉瘤的病理诊断提供了一种高效可靠的解决方案。本研究为医学图像分析做出了贡献，为医生提供了一种准确且有价值的显微诊断工具。它还推动了人工智能在医学成像技术中的发展。
