Prostate cancer diagnosis using quantitative phase imaging and machine learning algorithms

2015 ◽  
Author(s):  
Tan H. Nguyen ◽  
Shamira Sridharan ◽  
Virgilia Macias ◽  
Andre K. Balla ◽  
Minh N. Do ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Cancer Diagnosis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Prostate Cancer Diagnosis ◽  
Quantitative Phase

scholarly journals Automatic Gleason grading of prostate cancer using quantitative phase imaging and machine learning

2017 ◽  
Vol 22 (3) ◽  
pp. 036015 ◽  
Author(s):  
Tan H. Nguyen ◽  
Shamira Sridharan ◽  
Virgilia Macias ◽  
Andre Kajdacsy-Balla ◽  
Jonathan Melamed ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Gleason Grading

scholarly journals Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

2020 ◽  
Vol 25 (02) ◽  
pp. 1 ◽  
Author(s):  
Van K. Lam ◽  
Thanh C. Nguyen ◽  
Vy Bui ◽  
Byung Min Chung ◽  
Lin-Ching Chang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cancer Cells ◽  
Phase Imaging ◽  
Quantitative Phase Imaging ◽  
Quantitative Phase ◽  
Quantitative Scoring

scholarly journals Prediction of Prostate Cancer using Machine Learning Algorithms

2020 ◽  
Vol 8 (5) ◽  
pp. 5353-5362
Keyword(s):  
Prostate Cancer ◽  
Machine Learning ◽  
Logistic Regression ◽  
Random Forest ◽  
Missing Values ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Cancer Disease ◽  
The Individual

Background/Aim: Prostate cancer is regarded as the most prevalent cancer in the word and the main cause of deaths worldwide. The early strategies for estimating the prostate cancer sicknesses helped in settling on choices about the progressions to have happened in high-chance patients which brought about the decrease of their dangers. Methods: In the proposed research, we have considered informational collection from kaggle and we have done pre-processing tasks for missing values .We have three missing data values in compactness attribute and two missing values in fractal dimension were replaced by mean of their column values .The performance of the diagnosis model is obtained by using methods like classification, accuracy, sensitivity and specificity analysis. This paper proposes a prediction model to predict whether a people have a prostate cancer disease or not and to provide an awareness or diagnosis on that. This is done by comparing the accuracies of applying rules to the individual results of Support Vector Machine, Random forest, Naive Bayes classifier and logistic regression on the dataset taken in a region to present an accurate model of predicting prostate cancer disease. Results: The machine learning algorithms under study were able to predict prostate cancer disease in patients with accuracy between 70% and 90%. Conclusions: It was shown that Logistic Regression and Random Forest both has better Accuracy (90%) when compared to different Machine-learning Algorithms.

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