# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Model Selection and Training
#### 5.4.1 Role of Classification Models  
This section summarizes recent research on classification models, particularly in the healthcare context. A concise overview of these models is provided in Table 9, with areas for future exploration indicated as well.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Model Name** & **Brief Descriptions** & **Aspects to Address** \\ \hline LG-GNN [73] & Developed a local ROI-GNN with a self-attention-based pooling module to preserve brain region embeddings and detect biomarkers. Followed by a subject-GNN employing an adaptive weight aggregation block to generate multi-scale feature embeddings. & Further enhancements may be achieved by incorporating non-imaging data like genetic and intelligence quotient information. \\ \hline MuRCL [89] & Formulated the initial stage as contrastive learning to create negative/positive feature sets from patch-level WSI features. These sets are input to RL, where the agent updates selection based on online rewards for slice-level aggregation. & Significant training time and GPU resources may be required for model training. \\ \hline EfficientNet [118] & Modified the last layer of EfficientNet by adding global average pooling, batch normalization, dense layers, and dropout layers. & The model exhibits longer inference time (41 s) than others and increases parameters compared to original EfficientNetB4 due to additional dense layer. \\ \hline Ensembled classification model with Bayesian-optimized classifiers [119] & Ensembled nine pre-trained classification models (InceptionV3, Xception, Darknet19,Darknet53, DenseNet201, EfficientNetB0, NASNet Mobile, Resnet50, ResNet101) for initial feature extraction, followed by deep feature extraction through dense layers and Bayesian-optimized classification layers. & The model exhibits longer inference time (41 s) than others and increases parameters compared to original EfficientNetB4 due to additional dense layer. \\ \hline MobileNetv3 [120] & Hypertuned the model while keeping the original network structure intact & The model may exhibit potential biases in prediction. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Contemporary literature on the application of classification algorithms in healthcare.  
Other popular classification models include AlexNet [121], which achieved a significant success in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). This has led to the development of other popular models such as the Visual Geometry Group Network (VGGNet) [121], which embraces simplicity through a uniform architecture characterized by small 3 \(\times\) 3 convolutional filters, providing a sturdy framework for image recognition tasks. The VGGNet researchers experimented with the number of convolutional layers, resulting in variants such as VGG16 and VGG19, with 16 and 19 layers, respectively. CNN architectures have continued to unfold with the advent of Residual Networks (ResNets) [121], followed by inception modules such as Inception V3 [121], Inception V4 [121], and InceptionResNetV2 [121]. Meanwhile, refined iterations of the ResNet architecture, including ResNet50V2 [122], ResNet101V2 [122], and ResNet152V2 [122], have emerged, incorporating enhancements such as identity mappings and bottleneck blocks that further solidify their progress in image classification. Feature reusability and feature propagation are further enhanced with the inclusion of densely connected blocks in DenseNet [121], where each layer receives inputs from all preceding layers. The inception architecture has been further extended by Xception, in which the standard convolutional layers are replaced by depthwise separable convolutional layers.