Improved pulmonary lung nodules risk stratification in computed tomography images by fusing shape and texture features in a machine‐learning paradigm

2020 ◽  
Author(s):  
Satya Prakash Sahu ◽  
Narendra D. Londhe ◽  
Shrish Verma ◽  
Bikesh K. Singh ◽  
Sumit Kumar Banchhor
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Risk Stratification ◽  
Texture Features ◽  
Lung Nodules ◽  
Learning Paradigm ◽  
Computed Tomography Images

Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm

2017 ◽  
Vol 80 ◽  
pp. 77-96 ◽  
Author(s):  
Tadashi Araki ◽  
Pankaj K. Jain ◽  
Harman S. Suri ◽  
Narendra D. Londhe ◽  
Nobutaka Ikeda ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Stroke Risk ◽  
Plaque Morphology ◽  
Learning Paradigm ◽  
Carotid Wall

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

2021 ◽  
Author(s):  
S. Vishwa Kiran ◽  
Inderjeet Kaur ◽  
K. Thangaraj ◽  
V. Saveetha ◽  
R. Kingsy Grace ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Data Science ◽  
Processing Technique ◽  
Ct Images ◽  
Lung Cancer Diagnosis ◽  
Computed Tomography Images ◽  
Diagnosis And Classification

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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