The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a microscope). The purpose of this study is to use artificial intelligence to classify osteosarcoma histological images and to assess tumor survival and necrosis, which will help doctors reduce their workload, improve the accuracy of osteosarcoma cancer detection, and make a better prognosis for patients. The study proposes a typical transformer image classification framework by integrating noise reduction convolutional autoencoder and feature cross fusion learning (NRCA-FCFL) to classify osteosarcoma histological images. Noise reduction convolutional autoencoder could well denoise histological images of osteosarcoma, resulting in more pure images for osteosarcoma classification. Moreover, we introduce feature cross fusion learning, which integrates two scale image patches, to sufficiently explore their interactions by using additional classification tokens. As a result, a refined fusion feature is generated, which is fed to the residual neural network for label predictions. We conduct extensive experiments to evaluate the performance of the proposed approach. The experimental results demonstrate that our method outperforms the traditional and deep learning approaches on various evaluation metrics, with an accuracy of 99.17% to support osteosarcoma diagnosis.
翻译:肿瘤的恶性肿瘤程度及其转移/扩散趋势主要取决于病理等级(通过观察显微镜下肿瘤的形态学确定),这项研究的目的是利用人工智能对肿瘤的肿瘤形态学图象进行分类,并评估肿瘤的存活率和坏死性,这将有助于医生减少工作量,提高骨肿瘤癌症检测的准确性,并使病人有更好的预感。研究建议了典型的变异图像分类框架,将减少噪声的分流自动电离层和特征交叉融合学习(NRCA-FCFFL)结合起来,对骨肿瘤形态学图象进行分类。 Noise减少传动自动肿瘤形态学图象可以很好地腐蚀性肿瘤的遗传学图象,从而导致更纯度的血管肿瘤肿瘤肿瘤分类。此外,我们引入了特征交叉学习,将两个比例图象补分结合在一起,以便利用额外的分类符号充分探讨其相互作用。结果,形成了一种精细的聚合特征,将形成一种精细的特性,用来对骨质肿瘤进行分类分析。我们提出的实验性结果是,一种实验性模型的模型的模型,用来显示方法。