Abstract: Pattern recognition is the research area of Artificial
Intelligence that studies the operation and design of systems that
recognize patterns in the data. Important application areas are image
analysis, character recognition, fingerprint classification, speech
analysis, DNA sequence identification, man and machine
diagnostics, person identification and industrial inspection. The
interest in improving the classification systems of data analysis is
independent from the context of applications. In fact, in many
studies it is often the case to have to recognize and to distinguish
groups of various objects, which requires the need for valid
instruments capable to perform this task. The objective of this article
is to show several methodologies of Artificial Intelligence for data
classification applied to biomedical patterns. In particular, this work
deals with the realization of a Computer-Aided Detection system
(CADe) that is able to assist the radiologist in identifying types of
mammary tumor lesions. As an additional biomedical application of
the classification systems, we present a study conducted on blood
samples which shows how these methods may help to distinguish
between carriers of Thalassemia (or Mediterranean Anaemia) and
healthy subjects.
Abstract: A typical definition of the Computer Aided Diagnosis
(CAD), found in literature, can be: A diagnosis made by a radiologist
using the output of a computerized scheme for automated image
analysis as a diagnostic aid. Often it is possible to find the expression
Computer Aided Detection (CAD or CADe): this definition
emphasizes the intent of CAD to support rather than substitute the
human observer in the analysis of radiographic images. In this article
we will illustrate the application of CAD systems and the aim of
these definitions.
Commercially available CAD systems use computerized
algorithms for identifying suspicious regions of interest. In this paper
are described the general CAD systems as an expert system
constituted of the following components: segmentation / detection,
feature extraction, and classification / decision making.
As example, in this work is shown the realization of a Computer-
Aided Detection system that is able to assist the radiologist in
identifying types of mammary tumor lesions. Furthermore this
prototype of station uses a GRID configuration to work on a large
distributed database of digitized mammographic images.
Abstract: Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.