CADLCD2022: Current Applications of Deep Learning in Cancer Diagnostics |
Website | https://sites.google.com/view/cadlcd2022/home |
Submission link | https://easychair.org/conferences/?conf=cadlcd2022 |
CRC Press, Taylor & Francis (USA) invites chapter submission for the book titled as Current Applications of Deep Learning in Cancer Diagnostics.
Cancer diagnostics is a term used to describe a group of medical tests that are used to diagnose infections, disorders, and diseases related to human cancer. To obtain results, biological samples like as blood or tissue are extracted from the human body.
Several approaches have been developed in recent times to automatically diagnose different cancer conditions. These approaches can essentially be split into two types of hand-crafted features and classifier approaches based on standard instruction, respectively. The second solution is focused on completely automatic approaches based on deep learning. The first type uses manually segregated characteristics and is given to classifiers as data. In training, the classifiers do not change the functions. However, in the second category of attributes, parameters may be modified to execute unique training data activities. Deep learning does not use hand-crafted features and have successfully been adapted to solve the cancer diagnostic problems. As a result, deep learning is now playing an important role in the advancement of cancer diagnostics. This is the need for a new book in the field of cancer diagnostics based on deep learning.
Submission Guidelines
All chapters must be original and not simultaneously submitted to another journal or conference. The following chapter categories are welcome:
- Research Papers describing new techniques in the field of deep learning based cancer diagnostics
- Review Papers describing the systemetic literature review in the field of deep learning based cancer diagnostics
List of Topics (but not limited to)
- Introduction to current applications of deep learning in oncology
- Preprocessing of the oncology data using deep learning
- Review or evolution of deep learning techniques for human cancer
- Prediction of cancer susceptibility using deep learning
- Prediction of cancer recurrence using deep learning
- Prediction of the stage and grade of human cancer using deep learning
- Cancer detection using deep neural networks using deep learning
- Prediction of cancer from gene expression data using deep learning
- Tumor segmentation using deep learning
- Tracking tumor development using deep learning
- Prediction of cancer survival using deep learning
- Prediction of cancer prognosis using deep learning
- Challenges and future scopes in current applications of deep learning in cancer diagnostics
Key Points for Authors
- Each article length may be about 12-15 pages.
- Each chapter will consist of Abstract, Introduction, Proposed Method, Results and Discussion, Conclusion and References.
- Plagiarism percentage should be less than 12%.
Publication
CADLCD2022 chapters will be published by CRC Press, Taylor & Francis (USA)
Benefits of Authors
- There is NO processing/publication charge.
- CRC press will provide each corresponding author with one electronic copy free of cost.
- Worldwide circulation through CRC Press.
Important Dates
Abstract submission deadline: 20th November 2021
Chapter submission Deadline: 1st March 2022
Review notification: 30th March 2022
Revised chapter submission: 30th April 2022
Final decision notification: 15th May 2022
Submission of Final Chapters to Publisher: 30th May 2022
Contact
All questions about submissions should be emailed to Dr. Jyotismita Chaki, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. Email: jyotismita@vit.ac.in