Efficient segmentation and classification of the lung carcinoma via deep learning-Reference-Cited by-同舟云学术

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Efficient segmentation and classification of the lung carcinoma via deep learning

Published:2023-10-13 Issue: Volume: Page:
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Yamuna Devi M. M.,Jeyabharathi J.,Kirubakaran S.,Narayanan Sreekumar,Srikanth T.,Chakrabarti Prasun

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

https://link.springer.com/content/pdf/10.1007/s11042-023-17082-2.pdf

Reference16 articles.

1. Biswas A, Bhattacharya P, Maity SP (2020) An Efficient Semiautomatic Active Contour Model of Liver Tumor Segmentation from CT Images Computational Network Application Tools for Performance Management (pp. 75-85): Springer

2. Kamble B, Sahu SP, Doriya R (2020) A Review on Lung and Nodule Segmentation Techniques Advances in Data and Information Sciences (pp. 555-565): Springer

3. Khan MA, Rubab S, Kashif A, Sharif MI, Muhammad N, Shah JH, … Satapathy SC (2020) Lungs cancer classification from CT images: An integrated design of contrast based classical features fusion and selection. Pattern Recog Lett 129:77–85

4. Matava C, Pankiv E, Raisbeck S, Caldeira M, Alam F (2020) A Convolutional Neural Network for Real-Time Classification, Identification, and Labelling of Vocal Cord and Tracheal Using Laryngoscopy and Bronchoscopy Video. J Med Syst 44(2):1–10

5. Niyaz U, Sambyal AS, Padha D (2020) Evaluation of Deep Learning Model with Optimizing and Satisficing Metrics for Lung Segmentation Soft Computing for Problem Solving (pp. 67-78): Springer

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