## 2 Related Work
They devised a preprocessing technique that enhances the execution of machine learning procedures for the detection of liver illnesses. The article discusses the preparation procedures, ensemble techniques, and the dataset used. It was expected that the proposed preprocessing strategy will work better than standard methods, as shown by measures such as accuracy, compassion, specificity, and RUP. To develop an intelligent model using machine learning techniques to accurately forecast the first phases of liver disease, the authors of [24; 25; 26] developed a machine learning model to accurately forecast the occurrence of early liver disease by using a diverse range of features. The article describes the feature selection technique, the machine learning algorithm used, and the dataset utilized. The findings include the system's effectiveness in predicting early liver disease, as well as metrics such as accuracy, sensitivity, specificity, and RUP. The literature review highlights the advancements achieved in liver disease detection via the use of ML and DL approaches. Researchers have verified the efficacy of these methods in several facets of liver illness, encompassing fibrosis staging, liver cancer categorization, and the diagnosis of non-alcoholic liver disease [27; 28; 29], among others. Nevertheless, it is crucial to acknowledge and resolve certain constraints, such as the variability of diseases and the accessibility of standardized information, to enhance the precision and applicability of these models. An essential and challenging task during surgical procedures is the precise recognition of different types of tissues in real time. Study [30] used hyper spectral imaging (HSI) and machine learning to perform tissue segmentation in numerous medical fields. The process consisted of two main parts. In the first phase, the data were preprocessed to address the significant complexity of the spectrum's dimensionality and the variability of tissue spectra across patients, which are two common challenges in HSI. Principal Component Analysis (PCA) was used on the spectral domain of HSI data to eliminate anomalies, enhance the smoothness of the spectrum, standardize the data, and decrease the number of dimensions during the preprocessing phase. Gaussian filters were used at several stages of processing in the spatial domain. Subsequently, they used a refined machine learning model to partition the tissues. After a statistical evaluation of seven machine learning models employing three levels of geographical analysis, the most optimal model was chosen. Study [31] presented a novel deep learning methodology to precisely segment liver tumors and detect liver structures in CT imaging. The suggested method uses the LiTS17 database and has four sub layers and a fully connected layer that can differentiate between liver and liver tumors. Based on the LiTS17 dataset U-Net approach, the used method was able to obtain a higher accuracy in DC, mean, F1 score, and recall value. The proposed approach was further evaluated in a noisy setting, and the network demonstrated robustness over a diverse range of signal-to-noise ratios (SNRs). The proposed methodology has shown satisfactory and favorable results when compared to previous research. Study [32] presented an innovative method for precisely distinguishing liver tissue and tumors from CT scan volumes. The proposed methodology used a hybrid ResUNet strategy, integrating the ResNet and UNet architectures, to successfully address this difficulty. The study primarily used two intersecting models for liver segmentation and assessment of the region of interest. Liver segmentation was conducted to examine the liver via a volumetric abdominal CT image. The proposed model employs CT volume slices of patients diagnosed with liver tumors and was evaluated using the publicly accessible HSI dataset. The experimental investigation showed that the liver segmentation achieved a higher accuracy.