The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Enhanced Bone Cancer Detection Using Deep Convolutional Learning Classification From Histopathological Images
|
|
Author(s): Richa Singh (KIET Group of Institutions, Ghaziabad, India), Nidhi Srivastava (AIIT, Amity University, Lucknow, India)and Rekha Kashyap (KIET Group of Institutions, Ghaziabad, India)
Copyright: 2025
Pages: 18
Source title:
Assistive Technology Solutions for Aging Adults and Individuals With Disabilities
Source Author(s)/Editor(s): Rajiv Pandey (Amity University, Lucknow, India), Pratibha Maurya (Amity University, Lucknow, India), Jigna Bhupendra Prajapati (Ganpat University, India), Raju Halder (Indian Institute of Technology, Patna, India)and Kanishka Tyagi (UHV Technologies, USA)
DOI: 10.4018/979-8-3693-6308-9.ch010
Purchase
|
Abstract
The old procedures of the conventional histology process continue to limit pathologists. Bone structure is complex, which makes diagnosis difficult. To improve diagnostic capacities using CAD tools, digital histopathology must be used. The development using automated diagnostic techniques needs investigation based on the various facets of bone structure. To assess the tumor density CAD methods have been created still image classification remains a considerable difficult. To address this issue, the Convolutional algorithm is used for identifying different types of cancer using histopathology images. To differentiate between healthy & damaged bone sections, our approach combines important classifiers and applies the Karhunen-Loeve extraction method for certain picture attributes. The results found are subsequently fed with the help of ML to enhance the accuracy i.e. 97.3% of bone cancer detection prediction. The proposed method, applies algorithms of ML to histopathological analysis, provides a promising approach to increase the accuracy and effectiveness of bone cancer identification
Related Content
|
Saumya Srivastava.
© 2025.
26 pages.
|
|
Rajiv Pandey, Pratibha Maurya, Alpana Srivastava.
© 2025.
18 pages.
|
|
Mohsen Mahmoudi-Dehaki, Nasim Nasr-Esfahani, Srinivasan Vasan.
© 2025.
28 pages.
|
|
Durgansh Sharma.
© 2025.
16 pages.
|
|
Arun Verma, Madan Chandra Maurya.
© 2025.
28 pages.
|
|
G. V. S. Anil Chandra, S. Jeevan, Shantagoud Biradar, Ramya Raghavan.
© 2025.
26 pages.
|
|
Minal Dilip Kalamkar, Rajesh Prasad.
© 2025.
20 pages.
|
|
|