Year: 2011
Type: book-chapter
Abstract: Artificial neural networks (ANNs) play an essential role in the medical imaging field, including medical image analysis and computer-aided diagnosis (CAD) (Doi 2005;Giger and Suzuki 2007), because objects such as lesions and organs in medical images may not be represented accurately by a simple equation.For example, a lung nodule is generally modeled as a solid sphere, but there are nodules of various shapes and nodules with internal inhomogeneities, such as spiculated nodules and ground-glass nodules.A polyp in the colon is modeled as a bulbous object, but there are also polyps which exhibit a flat shape (Lostumbo, Wanamaker et al. 2010).Thus, diagnostic tasks in medical images essentially require "learning from examples (or data)."One of the most popular uses of ANNs in medical image analysis is the classification of objects such as lesions into certain classes (e.g., abnormal or normal, lesions or non-lesions, and malignant or benign).The task of ANNs here is to determine "optimal" boundaries for separating classes in the multi-dimensional feature space which is formed by input features (e.g., contrast, area, and circularity) obtained from object candidates.Machine-learning algorithms for classification include linear discriminant analysis (Fukunaga 1990), quadratic discriminant analysis (
Source: InTech eBooks
DOI: 10.5772/16084
Author: Kenji Suzuki
Institution: University of Chicago
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W1523708643