Extensive Multilabel Classification of Brain MRI Scans for Infarcts Using the Swin UNETR Architecture in Deep Learning Applications.

Accuracy for our multilabel data was derived as [ 30 ]: (5) Accuracy = TP + TN T P + T N + F P + F N RESULTS Outcomes of training Swin UNETR using the ISLES 2022 dataset During training of Swin UNETR with the ISLES 2022 dataset, the batch loss showed considerable fluctuation over 23,200 iterations but exhibited a decreasing trend as epochs progressed, reaching a value of 0.8885±0.1897 ( Table 2 , Fig. 1A ).

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