Abstract: Cardiologists perform cardiac auscultation to detect
abnormalities in heart sounds. Since accurate auscultation is
a crucial first step in screening patients with heart diseases,
there is a need to develop computer-aided detection/diagnosis
(CAD) systems to assist cardiologists in interpreting heart sounds
and provide second opinions. In this paper different algorithms
are implemented for automated heart sound classification using
unsegmented phonocardiogram (PCG) signals. Support vector
machine (SVM), artificial neural network (ANN) and cartesian
genetic programming evolved artificial neural network (CGPANN)
without the application of any segmentation algorithm has been
explored in this study. The signals are first pre-processed to remove
any unwanted frequencies. Both time and frequency domain features
are then extracted for training the different models. The different
algorithms are tested in multiple scenarios and their strengths and
weaknesses are discussed. Results indicate that SVM outperforms
the rest with an accuracy of 73.64%.
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: Mammography is the most effective procedure for an
early diagnosis of the breast cancer. Nowadays, people are trying to
find a way or method to support as much as possible to the
radiologists in diagnosis process. The most popular way is now being
developed is using Computer-Aided Detection (CAD) system to
process the digital mammograms and prompt the suspicious region to
radiologist. In this paper, an automated CAD system for detection
and classification of massive lesions in mammographic images is
presented. The system consists of three processing steps: Regions-Of-
Interest detection, feature extraction and classification. Our CAD
system was evaluated on Mini-MIAS database consisting 322
digitalized mammograms. The CAD system-s performance is
evaluated using Receiver Operating Characteristics (ROC) and Freeresponse
ROC (FROC) curves. The archived results are 3.47 false
positives per image (FPpI) and sensitivity of 85%.
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.