Image Processing for Knowledge Management and Effective Information Extraction for Improved Cervical Cancer Diagnosis
Safitri Jaya, M. Latha · 2021
Knowledge management in image processing is essential task for improving the medical images where as case-based reasoning (CBR) is the technique used for prediction purpose. This CBR is a type of Artificial Intelligence used for classification and regression problems to predict the upcoming results based on previous training samples. Information Extraction is a method for improving the visualization of the image by manipulating pixels and statistical elements. Image Interpretation is essential technique to identify the features in a object related to shape, color, texture, shadows, size, and patterns. In this chapter, the digital image processing role is discussed as well as knowledge management using CBR and information extraction using feature extraction algorithm and OBIA (Object-Based Image Analysis) for image segmentation. Feature/information extraction is implemented with the data set of cervical cancer using GLCM, HOG, and SURF algorithms. Machine learning algorithms and data science are the best tools for medical diagnosis for predicting the cancer state at the earliest. Data analysis is one of the initial steps to predict the knowledge of the data set. Information computing is a big deal about the process of hardware and software application based on the data analyzing, data storing, data retrieval, and manipulation of existing records of the patient. This chapter is entirely focused on data science, computational excellence, information computing, and society for information extraction of cervical cancer diagnosis. The Pap smear image is a microscopic image which is used for the entire chapter.