SCOPE OF THE BOOK

Machine learning and deep learning, a subset of artificial intelligence is making the health care industry smarter by reducing the data handling time and enhancing the accuracy in the prediction and diagnosis of diseases. The handling of enormous amount of medical data such as results of blood samples, scan reports of various imaging modalities such as X rays, MRI etc that occupy more space, has led to the concept of developing models that can handle the enormous amount data and produce the desired result as expected by the doctors for further diagnosis and the treatment. This ultimately reduces the computational time of handling the records with machine learning and deep learning. This book will serve as a bridge between the researchers, academicians, educators and students in exchanging their work and experiences in the emerging areas of the health sector which is very much trivial in the areas of pathology, identification and diagnosis of diseases, drug discovery and manufacturing, for the maintenance of better health records, AI assisted radiotherapy, pharmaceuticals and in other areas of health care sector. This interdisciplinary book will also set a trend to evolve new concepts and ideas, share issues and challenges faced during the research with probable solutions as applied to biomedical image processing and analysis integrated with computing techniques.

GENERAL GUIDELINES

Each chapter must have a minimum of 20 pages or more than 5000 words approx and should include as abstract in the range of 150-200 words. The material should not include any other authored copyright material e.g., figures, tables or charts. The chapter can be prepared using .DOC. The material should not include color text, or color figures, charts or tables.

The author should send their full chapter to https://easychair.org/conferences/?conf=crcbookchapter21 and a plagiarism report from either Ithenticate or Turnitin to crcbookchapter21@gmail.com. If the chapter qualifies the publisher’s guidelines, it will be recommended to include in the proposed book published by CRC Press, Taylor & Francis Group, FL, USA. There are no publication and processing charges. The book shall be submitted to the major abstract and in indexing databases.

TOPICS OF INTEREST

Topics to be discussed in this edited book include, but are not limited to:

1. Deep Learning architectures for Medical Diagnosis.

2. Convolutional Neural Network for the Classification of Images.

3. An Insights to the variants of Deep learning Algorithm and its Application

4. Federated machine learning – Concepts and applications in Health care Sector

5. Optimization of Deep learning Networks.

6. Machine Learning for Medical Diagnosis.

7. New Methods for Soft Computing Linked with Deep Learning.

8. Deep Learning Theories, Models and Tools to Enhance Health Information System.

9 Computational Intelligence in the Detection and the Diagnosis of Clinical and Biomedical Images.

10. Bio inspired Computing Based Deep Neural Networks.

11. Deep Learning for Image Segmentation and reconstruction.

12. Improving the Performance of Deep Networks in Medical Diagnosis.

13. Pattern Recognition.

14. Integration of AI and IoT for the prediction and analysis of Health Records.

IMPORTANT DATES

Full Chapter Submission: July 01, 2021

First Review Notification: August 15, 2021

Acceptance Notification: September 20, 2021

Camera Ready Submission: October 5, 2021

BOOK EDITORS