# Smart Healthcare: Exploring the Internet of Medical Things with Ambient Intelligence
## 5 Utilization of AI Analysis in the IoMT
### Data Preprocessing  
An AI system initially preprocesses acquired image or text data using common techniques such as normalization and scaling. Image data often exhibit significant variations in pixel intensities due to factors such as lighting conditions or exposure settings, which can adversely affect model performance, particularly in medical imaging. Therefore, pixel intensities are normalized and scaled and edge detection techniques are applied to enhance or highlight object boundaries for better analysis. Image enhancement methods include contrast enhancement techniques such as histogram equalization and contrast-limited adaptive histogram equalization (CLAHE), both of which improve contrast by redistributing pixel intensity values. Edge enhancement techniques emphasize object boundaries to make them more discernible, while unsharp masking and brightness correction through logarithmic or gamma adjustment are commonly used to improve visual quality. Noise removal techniques reduce unwanted random variations in pixel values. Common methods include Gaussian filtering, which smooths images, and median filtering, which effectively removes salt-and-pepper noise by replacing each pixel value with the median of the neighboring pixels. Advanced techniques such as bilateral filtering preserve edges while reducing noise, and wavelet-based denoising decomposes the image into different frequency components to remove noise selectively. The raw image data may be collected from different sensors, resulting in varied image resolutions. In the case of DL-based image analysis, models have specific image resolution requirements, which can range from 224 \(\times\) 224 to 512 \(\times\) 512 or larger depending on the model. Thus, all collected raw images must undergo resizing to ensure uniform resolution. Additionally, images may contain nonessential regions, especially in neuroimaging, where surrounding tissue areas may not be relevant to the analysis; removing these nonessential regions allows models to focus on the critical tissue parts, making automated cropping necessary. Moreover, \begin{table}
\begin{tabular}{l l} \hline \hline
**Organ** & **Description** \\ \hline Heart & Automated cardiac diagnosis challenge dataset (ACDC) [87] \\ \hline Lungs & Lung Nodule Analysis 2016 Challenge (LUNA 16) [88] \\  & The Cancer Genome Atlas - Lung (TCGA-Lung) [89] \\ \hline Breast & Digital Database for Screening Mammography (DDSM) [72] \\  & CBIS-DDSM (Curated Breast Imaging Subset of DDSM) [72] \\  & Breast ultrasound image (BUSI and BUS) [84] \\  & INbreast Dataset [90] \\  & Breast Cancer Semantic Segmentation (BCSS) [91] \\ \hline Colon & 2015 MICCAI Standard Segmentation challenge (GlaS) [92] \\  & Colorectal Adenocarcinoma Glands (CRAG) [92] \\  & RINGS [92] \\ \hline Skin & DermNet [72] \\  & International Skin Imaging Collaboration (SIC) Archive [93] \\  & Diverse Dermatology Images (DDI) [94] \\ \hline Bone & Musculoskeletal Radiographs (MURA) [95] \\  & Bone Age Assessment Dataset [96] \\ \hline Joint & KneeMRI Dataset [72] \\  & Osteoarthritis Initiative (OAI) [97] \\  & Shoulder MRI Dataset [98] \\ \hline Hand & Hand Gesture Recognition Database (HGDB) [99] \\  & Handwritten Digit Dataset (MNIST) [100] \\  & American Sign Language (ASL) Alphabet Image Dataset [101] \\ \hline Foot & Human Foot Keypoint Dataset [102] \\  & Footwormel Impression Database (FID) [103] \\ \hline Others & Synapse multi-organ segmentation dataset (Synapse) [87] \\  & Camelyon16 [99] \\  & Ultrasound-guided regional anesthesia (UGRA) [104] \\  & Carpal tunnel diagnosis (CTS) [104] \\  & Robust Medical Instrument Segmentation 2019 challenge (ROBUST-MIS) [105] \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._in real-life situations abnormal data typically appear less frequently than normal data. To address this imbalance, data augmentation techniques are employed to increase the number of training samples and enhance data variability during training.