Application of Artificial Intelligence to the Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: Algorithm Development and Validation

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Application of Artificial Intelligence to the Monitoring of Medication Adherence for Tuberculosis Treatment in Africa: Algorithm Development and Validation
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Artificialintelligence could reduce barriers to tuberculosis care universityofga

Figure 2. Illustration of the building block of ResNet, 3D ResNet, and 3D ResNext. BN: batch normalization; conv: convolution; F: number of feature channels; ReLu: rectified linear unit .

Apart from 3D ResNet and 3D ResNext, we also used Inception-v4 and Inflated 3D as our feature extractors. As a 2D CNN model, Inception-v4 is the fourth version of the Inception architecture network family. Compared to previous versions of the Inception family, Inception-v4 not only has a more uniformly simplified architecture and more inception modules but also absorbs the idea of residual connections from ResNet to form the new Inception block called residual inception blocks.

To better illustrate the effectiveness of deep learning models for medication adherence recognition, we used a traditional visual feature descriptor, histogram of oriented gradient [], as the replacement of the features extracted by DCNNs. HOG is a traditional descriptor that can generate handcrafted features directly from the images. The handcrafted feature was fed into the SVM for classification. In our pilot study, the SVM with HOG features was used as a baseline.

Table 3. Performance of the proposed deep learning framework under different convolution neural networks and histogram of oriented gradient .

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