Abstract: Diagnosis can be achieved by building a model of a
certain organ under surveillance and comparing it with the real time
physiological measurements taken from the patient. This paper deals
with the presentation of the benefits of using Data Mining techniques
in the computer-aided diagnosis (CAD), focusing on the cancer
detection, in order to help doctors to make optimal decisions quickly
and accurately. In the field of the noninvasive diagnosis techniques,
the endoscopic ultrasound elastography (EUSE) is a recent elasticity
imaging technique, allowing characterizing the difference between
malignant and benign tumors. Digitalizing and summarizing the main
EUSE sample movies features in a vector form concern with the use
of the exploratory data analysis (EDA). Neural networks are then
trained on the corresponding EUSE sample movies vector input in
such a way that these intelligent systems are able to offer a very
precise and objective diagnosis, discriminating between benign and
malignant tumors. A concrete application of these Data Mining
techniques illustrates the suitability and the reliability of this
methodology in CAD.
Abstract: Hepatocellular carcinoma, also called hepatoma, most
commonly appears in a patient with chronic viral hepatitis. In
patients with a higher suspicion of HCC, such as small or subtle
rising of serum enzymes levels, the best method of diagnosis
involves a CT scan of the abdomen, but only at high cost. The aim of
this study was to increase the ability of the physician to early detect
HCC, using a probabilistic neural network-based approach, in order
to save time and hospital resources.