Abstract
Digital Imaging and Communication in Medicine (DiCoM) is one of the key protocols for medical imaging and related data. It is implemented in various healthcare facilities. Lung cancer is one of the leading causes of death because of air pollution. Early detection of lung cancer can save many lives. In the last 5 years, the overall survival rate of lung cancer patients has increased, due to early detection. In this paper, we have proposed Zero-phase Component Analysis (ZCA) whitening and Local Binary Pattern (LBP) to enhance the quality of lung images which will be easy to detect cancer cells. Local Energy based Shape Histogram (LESH) technique is used to detect lung cancer. LESH feature extracts a suitable diagnosis of cancer from the CT scans. The Gini coefficient is used for characterizing lung nodules which will be helpful in Computed Tomography (CT) scan. We propose a Convolutional Neural Network (CNN) algorithm to integrate multilayer perceptron for image segmentation. In this process, we combined both traditional feature extraction and high-level feature extraction to classify lung images. The convolutional neural network for feature extraction will identify lung cancer cells with traditional feature extraction and high-level feature extraction to classify lung images. The experiment showed a final accuracy of about 93.27%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Cruzroa A, Caicedo JC, Gonzalez FA (2011) Visualpattern mining in histology image collections using a bag of features. Artifi Intell Med 52(2):91–106
He Y, Sang N, Huang R (2011) Local binary pattern histogram based texton learning for texture classification. In 18th IEEE international conference on image processing, 2011
Wen-Huang LK (2015) Computer-aided classification of lung nodules on computed tomography images via Deep Learning Technique. OncoTarg Ther
Rahul C, Ghansala KK (2018) Convolutional neural network (CNN) for image detection and recognition. In First International conference on secure cyber computing and communication, IEEE
Chaudhary A, Singh SS (2012) Lung cancer detectionon CT images using image processing. In International transaction on computing sciences, vol 4
Levner I, Zhangm H (2007) Classification driven watershed segmentation. In IEEE transction on image processing, vol 16, no 5
Ginneken BV, Romenyand BM, Viergever MA (2001) Computer- aided diagnosis in chest radiography: a survey. In IEEE, transactions on medical imaging, vol 20, no 12
Sharma D, Jindal G (2011) Identifying lung cancer using image processing techniques. In International conference on computational techniques and artificial intelligence (ICCTAI’2011), vol 17, pp 872–880
Nguyen HT et al. (2003) Water snakes: Energy-driven watershed segmentation. In IEEE transactions on pattern analysis and machine intelligence, vol 25 no 3, pp 330–342
Farag A, Ali A, Graham J, Elshazly S, Falk R (2011) Evaluation of geometric feature descriptors for detection and classification of lung nodules in low dose CT scans of the chest. In IEEE International Symposium, Chicago, IL, USA
Lin P-L, Huang P-W, Lee C-H, Wu M-T (2013) Automatic classification for solitary pulmonary nodule in CT image by fractal analysis based on fractional Brownian motion model. Pattern Recogn 46(12):3279–3287
Farag A, Elhabian S, Graham J, Farag A, Falk R (2010) Toward precise pulmonary nodule descriptors for nodule type classification. Medical Image Computing and Computer-Assisted Intervention. MICCAI 2010. Springer, Berlin, Germany
Wajid K, Hussain A (2016) Lung cancer detection using local energy-based shape histogram (lesh) feature extraction and cognitive machine learning techniques. In IEEE 15th international conference on cognitive computing, 2016
ZhiFei L, HuiFang D (2018) Medical image classification based on deep features extracted by deep model and statistic feature fusion with multilayer perceptron. 2061516, Hindawi
Wen-Huang KL (2015) Computer-aided classification of lung nodules on computed tomography images via Deep Learning Technique. OncoTarg Ther
Khan SA, Shunkun Y (2015) Effective and reliable framework for lung nodules detection from CT scan images. OncoTarg Ther
Madero H, Guadalupe V (2015) Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine. In Madero Orozco et al. (eds) Biomedical Engineering Online, vol 14, p 9
Usma N, Sambyal AS (2018) Advances in Deep Learning Techniques for Medical Image analysis. In 5th IEEE international conference on parallel, distributed and grid computing, 2018
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dominic, D., Balachandran, K. (2021). A Deep Learning Approach in Early Prediction of Lungs Cancer from the 2D Image Scan with Gini Index. In: Jat, D.S., Shukla, S., Unal, A., Mishra, D.K. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 132. Springer, Singapore. https://doi.org/10.1007/978-981-15-5309-7_11
Download citation
DOI: https://doi.org/10.1007/978-981-15-5309-7_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-5308-0
Online ISBN: 978-981-15-5309-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)