Table 5 Performance of schizophrenic speech detection using classic deep neural networks and the proposed Sch-net
From: Sch-net: a deep learning architecture for automatic detection of schizophrenia
Evaluated indicators | 95% CI | |||||
|---|---|---|---|---|---|---|
AlexNet | VGG16 | ResNet34 | DenseNet121 | Xception | Sch-net (ours) | |
Accuracy | 0.9272 | 0.9247 | 0.9439 | 0.9469 | 0.9503 | 0.9768 |
(0.9249,0.9295) | (0.9225,0.9269) | (0.9398,0.9480) | (0.9449,0.9489) | (0.9482,0.9524) | (0.9739,0.9797) | |
Precision | 0.9279 | 0.8937 | 0.9074 | 0.9555 | 0.9462 | 0.9639 |
(0.9226,0.9333) | (0.8900,0.8973) | (0.9024,0.9124) | (0.9516,0.9594) | (0.9421,0.9503) | (0.9585,0.9693) | |
Recall | 0.9268 | 0.9643 | 0.9890 | 0.9375 | 0.9551 | 0.9908 |
(0.9251,0.9285) | (0.9643,0.9643) | (0.9822,0.9958) | (0.9375,0.9375) | (0.9545,0.9556) | (0.9898,0.9918) | |
F1-score | 0.9273 | 0.9276 | 0.9463 | 0.9464 | 0.9506 | 0.9771 |
(0.9252,0.9293) | (0.9257,0.9295) | (0.9423,0.9503) | (0.9445,0.9483) | (0.9486,0.9526) | (0.9743,0.9799) | |
Sensitivity | 0.6399 | 0.8795 | 0.9798 | 0.9262 | 0.9482 | 0.9914 |
(0.6244,0.6554) | (0.8715,0.8875) | (0.9725,0.9870) | (0.9244,0.9280) | (0.9409,0.9556) | (0.9863,0.9964) | |
Specificity | 0.8747 | 0.9268 | 0.9399 | 0.9938 | 0.9646 | 0.9738 |
(0.8564,0.893) | (0.9164,0.9372) | (0.9331,0.9467) | (0.9910,0.9965) | (0.9567,0.9724) | (0.9656,0.9820) | |
AUC | 0.7935 | 0.9447 | 0.9888 | 0.9908 | 0.9924 | 0.9978 |
(0.7868,0.8003) | (0.9422,0.9472) | (0.9855,0.9921) | (0.9899,0.9917) | (0.9912,0.9936) | (0.9965,0.9990) | |