Title:A Survey on Machine Learning Based Medical Assistive Systems in Current
Oncological Sciences
Volume: 18
Author(s): Bobbinpreet Kaur , Bhawna Goyal*Ebenezer Daniel
Affiliation:
- Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, India
Keywords:
Machine intelligence, lung cancer, breast cancer, brain tumor, CAD, medical imaging.
Abstract:
Background: Cancer is one of the life-threatening diseases which is affecting a large
number of population worldwide. Cancer cells multiply inside the body without showing much
symptoms on the surface of the skin, thereby making it difficult to predict and detect the onset of
the disease. Many organizations are working towards automating the process of cancer detection
with minimal false detection rates.
Introduction: The machine learning algorithms serve to be a promising alternative to support
health care practitioners to rule out the disease and predict the growth with various imaging and statistical
analysis tools. Medical practitioners are utilizing the output of these algorithms to diagnose
and design the course of treatment. These algorithms are capable of finding out the risk level of the
patient and can reduce the mortality rate concerning cancer disease.
Method: This article presents the existing state of art techniques for identifying cancer affecting human
organs based on machine learning models. The supported set of imaging operations is also
elaborated for each type of cancer.
Conclusion: The CAD tools are the aid for the diagnostic radiologists for preliminary investigations
and detecting the nature of tumor cells.