Public articles linked to the same research event.
Primary health care research & development This study proposes a hybrid deep learning framework that fuses a static periocular ResNet18 classifier with a facial-image ensemble of ResNet50, EfficientNet-B0 and DenseNet121 under an OR-based parallel rule for early autism spectrum disorder risk indication and referral support; the periocular model reached 90% sensitivity, the facial ensemble reached 87.1% sensitivity with an AUC of 0.948, and under a conditional-independence assumption the analytically estimated system-level sensitivity was 0.9871, corresponding to a joint false-negative probability of about 1.29%, while system specificity fell to roughly 0.807.
This study proposes a hybrid deep learning framework that fuses a static periocular ResNet18 classifier with a facial-image ensemble of ResNet50, EfficientNet-B0 and DenseNet121 under an OR-based parallel rule for early autism spectrum disorder risk indication and referral support; the periocular model reached 90% sensitivity, the facial ensemble reached 87.1% sensitivity with an AUC of 0.948, and under a conditional-independence assumption the analytically estimated system-level sensitivity was 0.9871, corresponding to a joint false-negative probability of about 1.29%, while system specificity fell to roughly 0.807.
This study proposes a hybrid deep learning framework that fuses a static periocular ResNet18 classifier with a facial-image ensemble of ResNet50, EfficientNet-B0 and DenseNet121 under an OR-based parallel rule for early autism spectrum disorder risk indication and referral support; the periocular model reached 90% sensitivity, the facial ensemble reached 87.1% sensitivity with an AUC of 0.948, and under a conditional-independence assumption the analytically estimated system-level sensitivity was 0.9871, corresponding to a joint false-negative probability of about 1.29%, while system specificity fell to roughly 0.807.
This study proposes a hybrid deep learning framework that fuses a static periocular ResNet18 classifier with a facial-image ensemble of ResNet50, EfficientNet-B0 and DenseNet121 under an OR-based parallel rule for early autism spectrum disorder risk indication and referral support; the periocular model reached 90% sensitivity, the facial ensemble reached 87.1% sensitivity with an AUC of 0.948, and under a conditional-independence assumption the analytically estimated system-level sensitivity was 0.9871, corresponding to a joint false-negative probability of about 1.29%, while system specificity fell to roughly 0.807.