Deep3DSCan: Deep residual network and morphological descriptor based framework for lung cancer classification and 3D segmentation

With the increasing incidence rate of lung cancer patients, early diagnosis could help in reducing the mortality rate. However, accurate recognition of cancerous lesions is immensely challenging owing to factors such as low contrast variation, heterogeneity and visual similarity between benign and m...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	IET image processing Jg. 14; H. 7; S. 1240 - 1247
Hauptverfasser:	Bansal, Gaurang, Chamola, Vinay, Narang, Pratik, Kumar, Subham, Raman, Sundaresan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	The Institution of Engineering and Technology 29.05.2020
Schlagworte:	Special Section: Deep Learning for 3D Reconstruction image classification image fusion domain-specific features lung cancer classification malignant nodules publicly available LUNA16 dataset increasing incidence rate low contrast variation feature extraction image segmentation learning (artificial intelligence) computer aided design ensemble framework Deep3DSCan medical image processing cancerous lesions lung cancer patients reasonable scale invariance natural image segmentation benign nodules unseen situations fused features deep features handcrafted descriptors image matching morphological techniques deep residual network mortality rate lung early diagnosis fine-tuned residual network computerised tomography visual similarity domain agnostic nature lung cancer segmentation morphological descriptor based framework template matching technique deep learning techniques cancer minute differences
ISSN:	1751-9659, 1751-9667
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!