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

Makale detayları

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

Yazarlar:

Fuat TÜRK | Murat LÜY | Necaattin BARIŞÇI

Yayıncı:

Kirikkale University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

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

Belge Tipi:

Makale

Yayın Tarihi:

12/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000647103

Sıklık:

İki Yıllık

eISSN:

1308-5514

DOI:

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

Özet:

 

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.

Makale detayları

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

Yazarlar: Fuat TÜRK | Murat LÜY | Necaattin BARIŞÇI

Yayıncı: Kirikkale University

Ana Başlıklar: Uygulamalı fizik

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

Belge Tipi: Makale

Yayın Tarihi: 12/2019

Belge Dili: İngilizce

Document ID (Org No.): 2000647103

Sıklık: İki Yıllık

eISSN: 1308-5514

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

Sayı

Yazarlar:

Fuat TÜRK | Murat LÜY | Necaattin BARIŞÇI

Yayıncı:

Kirikkale University

Ana Başlıklar:

Uygulamalı fizik

Dergi:

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

Belge Tipi:

Makale

Yayın Tarihi:

12/2019

Belge Dili:

İngilizce

Document ID (Org No.):

2000647103

Sıklık:

İki Yıllık

eISSN:

1308-5514

DOI:

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