Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images
- Title
- Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images
- Creator
- Vishwa Kiran S.; Kaur I.; Thangaraj K.; Saveetha V.; Kingsy Grace R.; Arulkumar N.
- Description
- 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%. 2023 World Scientific Publishing Company.
- Source
- International Journal of Image and Graphics, Vol-23, No. 3
- Date
- 2023-01-01
- Publisher
- World Scientific
- Subject
- data science; disease diagnosis; image processing; Lung cancer; machine learning; predictive models
- Coverage
- Vishwa Kiran S., Department of AI and ML, BMS Institute of Technology and Management, Karnataka, Bangalore, 560064, India; Kaur I., Department of CSE, Ajay Kumar Garg Engineering College, Uttar Pradesh, Ghaziabad, 201009, India; Thangaraj K., Department of IT, Sona College of Technology, Tamil Nadu, Salem, 636005, India; Saveetha V., Department of IT, Dr. N. G. P Institute of Technology, Tamil Nadu, Coimbatore, 641048, India; Kingsy Grace R., Department of CSE, Sri Ramakrishna Engineering College, Tamil Nadu, Coimbatore, 641022, India; Arulkumar N., Department of Computer Science, CHRIST (Deemed to Be University), Karnataka, Bangalore, 560029, India
- Rights
- Restricted Access
- Relation
- ISSN: 2194678
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Vishwa Kiran S.; Kaur I.; Thangaraj K.; Saveetha V.; Kingsy Grace R.; Arulkumar N., “Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/14299.