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The degree of malignancy of osteosarcoma and its tendency tometastasize/spread mainly depend on the pathological grade (determined byobserving the morphology of the tumor under a microscope). The purpose of thisstudy is to use artificial intelligence to classify osteosarcoma histologicalimages and to assess tumor survival and necrosis, which will help doctorsreduce their workload, improve the accuracy of osteosarcoma cancer detection,and make a better prognosis for patients. The study proposes a typicaltransformer image classification framework by integrating noise reductionconvolutional autoencoder and feature cross fusion learning (NRCA-FCFL) toclassify osteosarcoma histological images. Noise reduction convolutionalautoencoder could well denoise histological images of osteosarcoma, resultingin more pure images for osteosarcoma classification. Moreover, we introducefeature cross fusion learning, which integrates two scale image patches, tosufficiently explore their interactions by using additional classificationtokens. As a result, a refined fusion feature is generated, which is fed to theresidual neural network for label predictions. We conduct extensive experimentsto evaluate the performance of the proposed approach. The experimental resultsdemonstrate that our method outperforms the traditional and deep learningapproaches on various evaluation metrics, with an accuracy of 99.17% to supportosteosarcoma diagnosis.

Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma