Brain tumor image segmentation using deep learning techniques / (Record no. 12342)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 03857cam a2200361 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 22760120 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240220105756.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS--GENERAL INFORMATION | |
| fixed length control field | m |o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr_||||||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220819s2023 flu ob 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
| LC control number | 2022039589 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Cancelled/invalid ISBN | 9780323911719 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | DLC |
| Language of cataloging | eng |
| Transcribing agency | DLC |
| Description conventions | rda |
| Modifying agency | DLC |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | pcc |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | RC270 |
| 060 00 - NATIONAL LIBRARY OF MEDICINE CALL NUMBER | |
| Classification number | QZ 241 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 616.99/4075 |
| Edition number | 23/eng/20220822 |
| 245 00 - TITLE STATEMENT | |
| Title | Brain tumor image segmentation using deep learning techniques / |
| Statement of responsibility, etc | edited by Jyotismita Chaki |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Cambridge, MA : |
| Name of publisher, distributor, etc | Academic Press, an imprint of Elsevier |
| Date of publication, distribution, etc | 2022 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xvi, 237 p. : |
| Other physical details | ill. (some col.) ; |
| Dimensions | 22 cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Contemporary Trends in the Early Detection and Diagnosis of Human Cancers using Deep Learning Techniques / Nirmala Vasan Balasenthilkumaran and Sumit Kumar Jindal -- Cancer data pre-processing techniques / Jyotismita Chaki -- A Survey on deep learning techniques for Breast, Leukemia and Cervical Cancer Prediction / N Jothiaruna and Anny Leema A -- An optimized deep learning technique for detecting lung cancer from CT images / Vanitha. M, Mangayarkarasi. R, Angulakshmi. M, and Deepa. M -- Brain tumor segmentation utilizing MRI multimodal images with deep learning / Chellaswamy C, Geetha T S, Markkandan S, and Thiruvalar Selvan -- Detection and Classification of Brain Tumors using Light Weight Convolutional Neural Network / Sabyasachi Mukherjee, Oishila Bandyopadhyay, and Arindam Biswas -- Parallel dense skip connected CNN approach for Brain Tumor Classification / G. Yogeswararao, V. Naresh, R. Malmathanraj, P. Palanisamy, and Karthik Balasubramanian -- Liver Tumor Segmentation Using Deep Learning Neural Networks / Sumedha Vadlamani, Charit Gupta Paluri, Jaydev Jangiti, Sumit Kumar Jindal -- Deep Learning Algorithms for Classification and Prediction of Acute Lymphoblastic Leukemia / Amrita Iand Snigdha Sen -- Cervical Pap smear Screening and Cancer Detection using Deep Neural Network / Munakala Lohith, Soumi Bardhan, and Oishila Bandyopadhyay -- Cancer detection using deep neural network: Differentiation of Squamous Carcinoma cells in Oral Pathology / Jayanthi Ganapathy -- Challenges and Future scopes in Current Applications of Deep Learning in Human Cancer Diagnostics / C.S. Vidhya, M. Loganathan, and R. Meenatchi. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "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"-- |
| 650 12 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Neoplasms |
| General subdivision | diagnosis |
| 9 (RLIN) | 23625 |
| 650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Deep Learning |
| 9 (RLIN) | 23626 |
| 650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Early Detection of Cancer |
| 9 (RLIN) | 23627 |
| 650 22 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Image Interpretation, Computer-Assisted |
| 9 (RLIN) | 15847 |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Chaki, Jyotismita, |
| Relator term | editor. |
| 9 (RLIN) | 23628 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Print version: |
| Title | Current applications of deep learning in cancer diagnostics |
| Edition | First edition. |
| Place, publisher, and date of publication | Boca Raton : CRC Press, Taylor & Francis Group, 2023 |
| International Standard Book Number | 9781032233857 |
| Record control number | (DLC) 2022039588 |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
| a | 7 |
| b | cbc |
| c | orignew |
| d | 1 |
| e | ecip |
| f | 20 |
| g | y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Item type | Books |
No items available.