logo

EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link:  https://ebookbell.com/faq 


We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.


For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Current Applications Of Deep Learning In Cancer Diagnostics Jyotismita Chaki

  • SKU: BELL-48050224
Current Applications Of Deep Learning In Cancer Diagnostics Jyotismita Chaki
$ 31.00 $ 45.00 (-31%)

4.0

16 reviews

Current Applications Of Deep Learning In Cancer Diagnostics Jyotismita Chaki instant download after payment.

Publisher: CRC Press
File Extension: PDF
File size: 15.66 MB
Pages: 189
Author: Jyotismita Chaki, Ayşegül Uçar
ISBN: 9781003277002, 1003277004
Language: English
Year: 2023

Product desciption

Current Applications Of Deep Learning In Cancer Diagnostics Jyotismita Chaki by Jyotismita Chaki, Ayşegül Uçar 9781003277002, 1003277004 instant download after payment.

"This book examines deep learning-based approaches in the field of cancer diagnostics, as well as pre-processing techniques which are essential to cancer diagnostics. Topics include: introduction to current applications of deep learning in cancer diagnostics; pre-processing of cancer data using deep learning; review of deep learning techniques in oncology; overview of advanced deep learning techniques in cancer diagnostics; prediction of cancer susceptibility using deep learning techniques; prediction of cancer reoccurrence using deep learning techniques; deep learning techniques to predict the grading of human cancer; different human cancer detection using deep learning techniques; prediction of cancer survival using deep learning techniques; complexity in the use of deep learning in cancer diagnostics; challenges and future scopes of deep learning techniques in oncology"--

Related Products