Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images

被引:2
作者
Kiran, S. Vishwa [1 ]
Kaur, Inderjeet [2 ]
Thangaraj, K. [3 ]
Saveetha, V. [4 ]
Grace, R. Kingsy [5 ]
Arulkumar, N. [6 ]
机构
[1] BMS Inst Technol & Management, Dept AI & ML, Bangalore 560064, Karnataka, India
[2] Ajay Kumar Garg Engn Coll, Dept CSE, Ghaziabad 201009, Uttar Pradesh, India
[3] Sona Coll Technol, Dept IT, Salem 636005, Tamil Nadu, India
[4] Dr NGP Inst Technol, Dept IT, Coimbatore 641048, Tamil Nadu, India
[5] Sri Ramakrishna Engn Coll, Dept CSE, Coimbatore 641022, Tamil Nadu, India
[6] CHRIST Deemed Univ, Dept Comp Sci, Bangalore 560029, Karnataka, India
关键词
Lung cancer; disease diagnosis; machine learning; data science; predictive models; image processing;
D O I
10.1142/S0219467822400022
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-the-art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%.
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页数:19
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