Machine Vision Techniques for Digital Mesothelioma Diagnostic System

The uncommon and aggressive cancer known as mesothelioma is mostly brought on by asbestos exposure. A prompt and correct mesothelioma diagnosis is essential for successful therapy and successful patient outcomes. The significant potential of machine vision algorithms to assist in the diagnosis of mesothelioma has recently been established through the analysis of digital images and pathological slides. The goal of this article is to increase the accuracy and efficacy of mesothelioma diagnosis by utilizing cutting-edge computer vision algorithms and image analysis methodologies. The capture of digital images of radiographic scans or histopathological slides is the first stage in the suggested diagnostic procedure. The photographs are then enhanced with pre-processing techniques to increase their clarity and quality. The images are then processed to extract characteristics including texture, shape, and colour using a range of techniques, including wavelet transformations, edge detection, and colour space conversions. Using machine learning techniques such as support vector machines (SVMs), artificial neural networks (ANN s), or convolutional neural networks (CNNs), categorization and diagnostic decision-making are carried out after the characteristics are obtained. Each image in the dataset used to train these algorithms corresponds to a certain diagnosis (mesothelioma or non-mesothelioma), and it consists of a collection of tagged photographs. The diagnostic system is validated and evaluated using a distinct dataset for sensitivity, specificity, accuracy, and other important metrics. By combining machine vision techniques into the diagnostic procedure, the digital mesothelioma diagnostic system seeks to improve the precision, objectivity, and effectiveness of mesothelioma diagnosis. This technique has the potential to assist medical professionals in cases of mesothelioma in a way that will lead to earlier discovery, individualized treatment planning, and improved patient outcomes.