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The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: The nnet architecture of the radiomics-based SCLC/NSCLC classifier. This figure presents the input layer with 20 nodes receiving 20 radiomic features, the 3 hidden layers for non-linear mapping, and the output layer with 2 nodes for “SCLC” and “NSCLC” decision upon a hard thresholding f(node)>0 and f(node)≤0, respectively. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques:

Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Journal: Frontiers in Oncology

Article Title: Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach

doi: 10.3389/fonc.2020.00593

Figure Lengend Snippet: Two scenarios for demonstrating the nnet “training-validating-testing” performance. Upper: one case of 1 misclassification; lower: one case of no misclassification. The panels designated as a1 and a2 present the nnet training behaviors under random initial settings (w: weight and b: bias); The panels designated as b1 and b2 present the output node values (in value range [−1,1], in black dots) in reference to target setting (SCLC = 1, NSCLC = -1); and the panels designated as c1 and c2 present the confusion matrices. SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer.

Article Snippet: For the SCLC/NSCLC classification (a typical 2-class problem) from high-dimensional features in a number of tens to thousands as in our study, we used multilayer artificial neural network classifiers ( https://www.mathworks.com/help/stats/machine-learning-in-matlab.html ), which in principle could achieve more optimal arbitrary non-linear mapping (e.g., non-linearity beyond analytic description or mathematical tracking) with appropriate configuration and training.

Techniques: