Image Analysis for Health Prediction

Image analysis is a collection of procedures that allow the investigator to extract quantitative data from morphological preparations. The appropriate use of quantitative image analysis of histological sections can yield valuable insights and be sensitive to even the slightest changes in the properties of tissue staining. But picture analysis might yield inaccurate findings if not done appropriately. Several of these mistakes can be unintentionally introduced with significant consequences since image analysis tools heavily rely on computer processing. Image analysis entails breaking the image down into its basic components to extract useful information from the image. Image analysis encompasses a variety of tasks, such as locating forms, recognizing edges, removing noise, counting objects, and calculating statistics for texture analysis or image quality. Image analysis has a variety of advantages. Algorithms give higher accuracy because they produce more exact and even continuous quantitative measures than humans. Image algorithms also allow for standardization owing to more repeatable findings, especially for intermediate rating categories and complicated scoring systems. Pathologists can save time by using automated image analysis, especially when doing monotonous activities such as counting. Image analysis has the potential to introduce computer‐aided design (CAD) by assisting pathologists in detecting, and diagnosing the disease. Some deep learning and complicated neural network methods have even been demonstrated to aid in health prediction. This chapter briefly discusses image analysis which has image processing, image segmentation, feature extraction and image classification for health prediction. CNN's medical image categorization outperforms human vision in identifying abnormalities in X‐ray and MRI pictures. These devices can show both the collection of images and their variations. The precise detection of objects inside images made possible by CNN image classification has revolutionized the area of computer vision. CNNs are an effective tool for a wide range of applications due to their capacity to automatically learn and extract complicated characteristics.