Multi-level classification of knee cartilage lesion in multimodal MRI based on deep learning

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The regeneration and repair ability of knee cartilage is limited, and the early clinical symptoms of patients are not obvious, so the diagnosis of knee cartilage damage is crucial for clinical treatment. To effectively overcome the irreversible injury caused by minimally invasive arthroscopic knee surgery, a multi-classification model of knee cartilage injury based on deep learning is proposed. The model has the characteristic of multi-feature fusion, which can realize automatic non-invasive monitoring and examination of knee cartilage injury.Furthermore, the proposed algorithm uses dragonfly optimization and regional similarity transformation algorithms to extract valid regional information on knee cartilage, cartilage edema, and subchondral bone in different modalities and integrates it into global multiscale features. It can obtain accurate information on the edges of knee cartilage and adjacent confusing areas, and solve the problem of less authentic medical images in hospitals for data enhancement, to realize an accurate network model of knee cartilage injury classification. The proposed algorithm performs a five-level classification of authentic hospital data sets with an accuracy of 99.73%. The experimental results show that the proposed model is generally higher than the current state-of-the-art classification depth model. Keywords: Knee cartilage lesion; Multilevel classification; Deep learning; Multimodal features.