Automatic detection of liver cancer- A review
Gahizi Emmanuel,Margaretha Sulistyoningsih

Universitas Atma Jaya Yogyakarta
Universitas Atma Jaya Yogyakarta

Abstract

Nowadays, Automatic liver detection is not easy
especially when it comes to the segmentation accuracy result
which can robust the process. There are much technique helps
for liver segmentation that has the ability to segment the liver
and have different ways of grouping them. This Research aims
at giving an overview of an aspiring method of automatic liver
detection used in the previous research. Categorizing the
classification method was based on the results and how it was
performed. Hence, the best result that leads to the detection of
liver cancer was based on the performance of every classifier.
Furthermore, this paper discusses the Artificial Neural Network
(ANN) and Support Vector Machine (SVM) as classification
techniques used for classifying liver cancer. Lastly, from the
results, the performance comparison between the two
mentioned classification techniques made accompanied by
rules and remarks for better solutions. As the results show, the
two classifier techniques are effective for liver cancer detection
which will help patients to know their liver cancer status.

Keywords: automatic detection, Liver disease, classifier, ANN, SVM.

Topic: Information Engineering