Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features

Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript d...

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Vydáno v:Journal of medical systems Ročník 43; číslo 12; s. 332
Hlavní autoři: Saba, Tanzila, Sameh, Ahmed, Khan, Fatima, Shad, Shafqat Ali, Sharif, Muhammad
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.12.2019
Springer Nature B.V
Témata:
Algorithms
Consortia
Experimentation
Feature extraction
Health Informatics
Health Informatics and Computer Vision
Health Sciences
Humans
Image & Signal Processing
Image databases
Image processing
Image segmentation
Lung cancer
Lung Neoplasms - diagnosis
Lung Neoplasms - diagnostic imaging
Lung Neoplasms - pathology
Machine Learning
Medical imaging
Medicine
Medicine & Public Health
Radiographic Image Interpretation, Computer-Assisted - methods
Recent Advances in Deep Learning for Biomedical Signal Processing
Statistics for Life Sciences
Tomography, X-Ray Computed - methods
VGG 19
SVM
Texture
Cells
Benign
ISSN:0148-5598, 1573-689X, 1573-689X
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Shrnutí:Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript depicts an approach of finding lung nodule at an initial stage that comprises of three major phases: (1) lung nodule segmentation using Otsu threshold followed by morphological operation; (2) extraction of geometrical, texture and deep learning features for selecting optimal features; (3) The optimal features are fused serially for classification of lung nodule into two categories that is malignant and benign. The lung image database consortium image database resource initiative (LIDC-IDRI) is used for experimentation. The experimental outcomes show better performance of presented approach as compared with the existing methods.
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ISSN:0148-5598
1573-689X
1573-689X
DOI:10.1007/s10916-019-1455-6