Research Article
Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images
Table 4
Recent studies in the literature with ASD.
| Studies | Method | Year | Modality | Accuracy (%) |
| Zhao et al. [12] | SVM | 2020 | f-MRI | 83 | Mostafa et al. [72] | LDA | 2019 | f-MRI | 77.70 | Liu et al. [73] | DFC + MTFS | 2020 | rs-f-MRI | 76.80 | Wang et al. [74] | MLP + ensemble learning | 2020 | f-MRI | 74.52 | Sun et al. [75] | No superparameter FCN | 2021 | rs-f-MRI | 71.74 | Shi et al. [76] | Three-way decision model | 2021 | f-MRI | 71.35 | Grana and Silva [77] | SVC | 2021 | rs-f-MRI | 71.10 | Spera et al. [78] | L-SVM | 2019 | rs-f-MRI | 71 | Reiter et al. [79] | RF | 2021 | rs-f-MRI | 67.81 | Chaitra et al. [80] | RCE-SVM | 2020 | f-MRI | 67.30 | Brahim and Farrugia [81] | RBF-SVC | 2020 | rs-f-MRI | 66.70 | Sun et al. [82] | RBF + SVM | 2021 | f-MRI | 59.10 | Kazeminejad and Sotero [83] | NEG + MLP | 2020 | rs-f-MRI | 58.70 | Dekhil et al. [15] | Correlation analysis | 2021 | rs-f-MRI | 95–100 | Dekhil et al. [18] | k-NN, random forest | 2019 | s-MRI, f-MRI | 81 | Jamwal et al. [84] | DBN | 2022 | s-MRI, f-MRI | — | Traut et al. [85] | Logistic regression, SVC | 2022 | s-MRI, f-MRI | 79 | Dadi et al. [86] | K-means, wards algorithm, CanICA, DictLearn | 2019 | rs-f-MRI | 86 | Abraham et al. [87] | K-means, wards algorithm, ICA | 2017 | rs-f-MRI | 67 | Gogoi et al. [88] | VGG16, inception v3, ResNet50 | 2023 | MRI | 94 | Han et al. [89] | Cross, supervised, LOSO, Fed_DA, | 2023 | rs-f-MRI, s-MRI | 69.37 | Manikantan and Jaganathan [90] | Graphical neural networks | 2023 | rs-f-MRI, s-MRI | 69.45 | Deng et al. [91] | GAN | 2023 | f-MRI | 71 | Dhinagar et al. [92] | Metalearning | 2023 | MRI | 85.70 | Artiles et al. [93] | Multiple linear regression | 2023 | rs-f-MRI | 76.40 | Quiang et al. [94] | Hierarchical FBN | 2023 | f-MRI | 82.10 | Jönemo et al. [95] | 3D CNN | 2023 | rs-f-MRI | 80 | Kunda et al. [96] | MIDA, ridge classifier, logistic regression, SVM | 2023 | rs-f-MRI | 73 |
|
|
SVM: support vector machine; LDA: linear discriminant analysis; DFC: dynamic functionally connected; MTFS: multitask feature selection; MLP: multilayer perceptron; FCN: functionally connected network; L-SVM: linear kernel support vector machine; RF: random forest; RCE: recursive cluster removal; RBF: radial basis function; NEG: negative correlation matrix; k-NN: k-nearest neighbors; DBN: deep belief network; CanICA: canonical independent component analysis; DictLearn: dictionary learning; ICA: independent component analysis; VGG: very deep convolutional networks; LOSO: leave one site out; Fed_DA: federated domain adaptation; GAN: adversarial generation network; FBN: functional brain networks; CNN: convolutional neural network; MIDA: maximum independence domain adaptation.
|