Deep learning for lung Cancer detection and classification

Lung cancer is one of the main reasons for death in the world among both men and women, with an impressive rate of about five million deadly cases per year. Computed Tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. The main objective of this work is to detect...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 79; no. 11-12; pp. 7731 - 7762
Main Authors Asuntha, A., Srinivasan, Andy
Format Journal Article
LanguageEnglish
Published New York Springer US 01.03.2020
Springer Nature B.V
Subjects
Algorithms
Computed tomography
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Feature extraction
FPSO
Histograms
Image classification
Image detection
Lung cancer
Lung diseases
Machine learning
Multimedia Information Systems
Nodules
Particle swarm optimization
Special Purpose and Application-Based Systems
Wavelet transforms
Deep learning
Classifiers
CNN
Lung cancer
Real-time
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Summary:Lung cancer is one of the main reasons for death in the world among both men and women, with an impressive rate of about five million deadly cases per year. Computed Tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. The main objective of this work is to detect the cancerous lung nodules from the given input lung image and to classify the lung cancer and its severity. To detect the location of the cancerous lung nodules, this work uses novel Deep learning methods. This work uses best feature extraction techniques such as Histogram of oriented Gradients (HoG), wavelet transform-based features, Local Binary Pattern (LBP), Scale Invariant Feature Transform (SIFT) and Zernike Moment. After extracting texture, geometric, volumetric and intensity features, Fuzzy Particle Swarm Optimization (FPSO) algorithm is applied for selecting the best feature. Finally, these features are classified using Deep learning. A novel FPSOCNN reduces computational complexity of CNN. An additional valuation is performed on another dataset coming from Arthi Scan Hospital which is a real-time data set. From the experimental results, it is shown that novel FPSOCNN performs better than other techniques.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08394-3