scholarly journals Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis

2016 ◽  
Vol 15 (2) ◽  
Author(s):  
B. Zhang ◽  
X.L. Liang ◽  
H.Y. Gao ◽  
L.S. Ye ◽  
Y.G. Wang
Keyword(s):  
Neural Network ◽  
Colorectal Cancer ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Tumor Markers ◽  
Cancer Diagnosis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Support Vector

Ovarian cancer diagnosis using pretrained mask CNN-based segmentation with VGG-19 architecture

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Kavitha Senthil ◽  
Vidyaathulasiramam
Keyword(s):  
Neural Network ◽  
Ovarian Cancer ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Random Forest ◽  
Cancer Diagnosis ◽  
Support Vector ◽  
Hybrid Neural Network ◽  
Cancer Prediction ◽  
The Neural Network

Abstract Objectives This paper proposed the neural network-based segmentation model using Pre-trained Mask Convolutional Neural Network (CNN) with VGG-19 architecture. Since ovarian is very tiny tissue, it needs to be segmented with higher accuracy from the annotated image of ovary images collected in dataset. This model is proposed to predict and suppress the illness early and to correctly diagnose it, helping the doctor save the patient's life. Methods The paper uses the neural network based segmentation using Pre-trained Mask CNN integrated with VGG-19 NN architecture for CNN to enhance the ovarian cancer prediction and diagnosis. Results Proposed segmentation using hybrid neural network of CNN will provide higher accuracy when compared with logistic regression, Gaussian naïve Bayes, and random Forest and Support Vector Machine (SVM) classifiers.

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