Machine Learning of Kidney Tumors and Diagnosis and Classification by Deep Learning Methods

Fuat TÜRK; Murat LÜY; Necaattin BARIŞÇI

doi:https://doi.org/10.29137/umagd.640667

Article Details

Machine Learning of Kidney Tumors and Diagnosis and Classification by Deep Learning Methods

Authors:

Fuat TÜRK | Murat LÜY | Necaattin BARIŞÇI

Publisher:

Kirikkale University

Main Topics:

Applied physics

Journal:

International Journal of Engineering Research and Development 2019 Vol.11 Issue 3, pp.802-812

Document Type:

Article

Publication Date:

12/2019

Document Language:

English

Document ID (Org No.):

2000647103

Frequency:

Bi-Annual

eISSN:

1308-5514

DOI:

https://doi.org/10.29137/umagd.640667

Abstract:

 

Kidney cancer is
difficult to diagnose and it can be quite complicated for physicians to
diagnose. In this study, while providing information about multiple sources to
help people who are dealing with the challenges of the diagnosis of kidney
cancer, in order to serve as a guide the principles of kidney cancer are tried
to be explained. In recent years, many new methods of treatment have been
developed for kidney cancer, and some are under development by scientists.
These studies provide treatment information that offers new hope to the lives
of kidney cancer patients. In this study, it is aimed to get acquainted with
kidney cancer cells by using machine learning, and deep learning algorithms. In
this way, an application can be developed to guide patients and physicians
through early diagnosis and classification.

Article Details

Machine Learning of Kidney Tumors and Diagnosis and Classification by Deep Learning Methods

Authors: Fuat TÜRK | Murat LÜY | Necaattin BARIŞÇI

Publisher: Kirikkale University

Main Topics: Applied physics

Journal: International Journal of Engineering Research and Development 2019 Vol.11 Issue 3, pp.802-812

Document Type: Article

Publication Date: 12/2019

Document Language: English

Document ID (Org No.): 2000647103

Frequency: Bi-Annual

eISSN: 1308-5514

DOI: https://doi.org/10.29137/umagd.640667

Issues

Authors:

Fuat TÜRK | Murat LÜY | Necaattin BARIŞÇI

Publisher:

Kirikkale University

Main Topics:

Applied physics

Journal:

International Journal of Engineering Research and Development 2019 Vol.11 Issue 3, pp.802-812

Document Type:

Article

Publication Date:

12/2019

Document Language:

English

Document ID (Org No.):

2000647103

Frequency:

Bi-Annual

eISSN:

1308-5514

DOI:

https://doi.org/10.29137/umagd.640667