1naresh
Array ( [urn:ac.highwire.org:guest:identity] => Array ( [runtime-id] => urn:ac.highwire.org:guest:identity [type] => guest [service-id] => ajnr-ac.highwire.org [access-type] => Controlled [privilege] => Array ( [urn:ac.highwire.org:guest:privilege] => Array ( [runtime-id] => urn:ac.highwire.org:guest:privilege [type] => privilege-set [privilege-set] => GUEST ) ) [credentials] => Array ( [method] => guest ) ) ) 1nareshArray ( [urn:ac.highwire.org:guest:identity] => Array ( [runtime-id] => urn:ac.highwire.org:guest:identity [type] => guest [service-id] => ajnr-ac.highwire.org [access-type] => Controlled [privilege] => Array ( [urn:ac.highwire.org:guest:privilege] => Array ( [runtime-id] => urn:ac.highwire.org:guest:privilege [type] => privilege-set [privilege-set] => GUEST ) ) [credentials] => Array ( [method] => guest ) ) )Table 2:Section-level and patient-level test results for ViT, ResNet50, and DenseNet121 models
ViT ResNet50 DenseNet121 Section-level classification accuracy 82% 72% 78% Patient-level accuracy 93% 62% 62% Patient-level sensitivity 0.90 1.0 0.8 Patient-level specificity 0.95 0.42 0.52 Patient-level PPV 0.90 0.47 0.47 Patient-level NPV 0.95 1.0 0.83