Laser-tissue interactions: A comparative analysis on synthetic and realistic datasets using machine learning and deep neural network techniques

In recent years, the use of lasers has increased in many applications, including highly sensitive applications such as tissue lasers. These applications require high precision due to their direct interaction with biological tissue. They also require a thorough understanding of the physical properties of the laser and its effects on biological tissue. Understanding laser parameters, selecting the most important and influential parameters, and developing a system capable of evaluating the classification process are essential to ensure the most appropriate use of lasers in clinical applications. This study presents a new, high-quality dataset, publicly available to researchers, divided into two parts: the synthetic dataset, which simulates ideal laser conditions, and the realistic dataset, which simulates realistic laser conditions in terms of some noise. The dataset, both synthetic and realistic, contains many important properties of laser-tissue interactions, such as wavelength, pulse duration, thermal conductivity, and other features. The features are classified relative to the laser beam to select the best and most effective features for the tissue using XGBoost and SHAP before being used with classifiers. The dataset provided high accuracy when evaluated using six different classifiers: three modern classifiers and three traditional classifiers. This study aims to present a comprehensive workflow, from data generation to results acquisition and analysis.