scholarly journals Early lung cancer diagnostic biomarker discovery by machine learning methods

2021 ◽  
Vol 14 (1) ◽  
pp. 100907
Author(s):  
Ying Xie ◽  
Wei-Yu Meng ◽  
Run-Ze Li ◽  
Yu-Wei Wang ◽  
Xin Qian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Biomarker Discovery ◽  
Diagnostic Biomarker ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Cancer Diagnostic ◽  
Early Lung Cancer

scholarly journals Machine Learning Methods for Breast Cancer Diagnostic

2018 ◽  
Author(s):  
Shahnorbanun Sahran ◽  
Ashwaq Qasem ◽  
Khairuddin Omar ◽  
Dheeb Albashih ◽  
Afzan Adam ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Cancer Diagnostic

scholarly journals Classification of Pulmonary CT Images by Using Hybrid 3D-Deep Convolutional Neural Network Architecture

2019 ◽  
Vol 9 (5) ◽  
pp. 940 ◽  
Author(s):  
Huseyin Polat ◽  
Homay Danaei Mehr
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Lung Cancer ◽  
Feature Extraction ◽  
Extraction Methods ◽  
Ct Images ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Diagnosis Of Lung Cancer ◽  
3D Cnn

Lung cancer is the most common cause of cancer-related deaths worldwide. Hence, the survival rate of patients can be increased by early diagnosis. Recently, machine learning methods on Computed Tomography (CT) images have been used in the diagnosis of lung cancer to accelerate the diagnosis process and assist physicians. However, in conventional machine learning techniques, using handcrafted feature extraction methods on CT images are complicated processes. Hence, deep learning as an effective area of machine learning methods by using automatic feature extraction methods could minimize the process of feature extraction. In this study, two Convolutional Neural Network (CNN)-based models were proposed as deep learning methods to diagnose lung cancer on lung CT images. To investigate the performance of the two proposed models (Straight 3D-CNN with conventional softmax and hybrid 3D-CNN with Radial Basis Function (RBF)-based SVM), the altered models of two-well known CNN architectures (3D-AlexNet and 3D-GoogleNet) were considered. Experimental results showed that the performance of the two proposed models surpassed 3D-AlexNet and 3D-GoogleNet. Furthermore, the proposed hybrid 3D-CNN with SVM achieved more satisfying results (91.81%, 88.53% and 91.91% for accuracy rate, sensitivity and precision respectively) compared to straight 3D-CNN with softmax in the diagnosis of lung cancer.

Classification of lung cancer using ensemble-based feature selection and machine learning methods

2015 ◽  
Vol 11 (3) ◽  
pp. 791-800 ◽  
Author(s):  
Zhihua Cai ◽  
Dong Xu ◽  
Qing Zhang ◽  
Jiexia Zhang ◽  
Sai-Ming Ngai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Learning Methods ◽  
Machine Learning Methods

The ensemble-based feature selection method presents the merit of acquisition of more informative and compact features than those obtained by individual methods.

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