# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Model Selection and Training
#### 5.4.3 The Role of Object Detection Models  
Object detection involves identifying objects and providing their precise location within an image. Methods for object detection can generally be categorized into one-stage and two-stage methods. One-stage detectors directly predict the bounding boxes and class labels for objects in a single step. They are typically faster, but may sacrifice some accuracy compared to two-stage detectors. Two-stage detectors, on the other hand, first propose RoIs in a region proposal network (RPN), then refine these proposals to detect objects. They often achieve higher accuracy, but may be slower due to the additional processing step. Examples from the contemporary literature on object detectors in the healthcare domain are provided in Table 13.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model Name** & **Brief Descriptions** & **Aspects to Address** \\ \hline YOLOv6 [88] & YOLOv6 model weight was optimized using particle swarm optimization. & The model employs a softmax classification layer to classify lung cancers. This layer’s performance can be compared with popular classifiers like SVM, Naive Bayes, and k-NN. \\ \hline RetinaNet [135] & The anchor size was changed to 8,10,10 and 15,14,14, respectively & The model needs additional training with post-circulation data and noisy data to address variations during image acquisition. \\ \hline YOLOv8 [136] & The model underwent hyperparameter tuning while preserving the original network properties. & The study’s single-institution basis limits generalizability. Improving model performance may involve integrating tumor information and multi-phase CT data. \\ \hline YOLO5 + UNet++ [137] & YOLOv5 detected renal cysts, which served as input for UNEt++ to predict saliency maps and salient landmarks. & The study’s restriction to a single institution and limited imaging device variation restricts generalizability. \\ \hline CRDet [138] & Employed a multi-scale feature extraction network with shunted self-attention and FPN, utilizing circular representation for improved morphological feature utilization of granulomas. & The model’s training and evaluation were conducted on a small dataset of \(50\) patents, potentially impacting its generalization. \\ \hline CASMatching + RetinaNet [139] & Utilized a two-stage pipeline: first, identifying stenotic morphological indices via RetinaNet, and second, employing a regression model to predict indices using the novel 2 DIoU loss. CASMatching then predicts a match score for normal and stenotic regions for quantifying the degree of stenosis through regression. \\ \hline \hline \end{tabular}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{2}{p{113.8 pt}}{SVM: Support Vector Machine; k-NN: k-Nearest Neighbors; CASMatching: Carotid Artery Stenosis Matching; 2 DIoU: Direction Distance-IoU; YOLO: You Only Look Once.} \\ \hline \hline \end{tabular}
\end{table}
Table 13: Contemporary literature on the application of object detection in healthcare.