Multiclass Classification of Prostate Cancer Gleason Grades Groups Using Features of multi parametric-MRI (mp-MRI) Images by Applying Machine Learning Techniques

Virk, Ishpreet Singh; Maini, Raman

doi:10.1109/aisc56616.2023.10085270

Cited by 2 publications

(1 citation statement)

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“…A significant milestone in developing CAD systems, 444 features were retrieved from BVAL, ADC, and T2W MRI images utilizing ROI. SVM classification beat the other classifiers with an accuracy of 44.64 percent, an FPR of 0.1604, and a PPVGG>1 value of 0.75 [33]. Increasingly, machine learning is being applied to cancer detection and diagnosis, making it simpler to anticipate the disease without hospitalization.…”

Section: Literature Reviewmentioning

confidence: 99%

Prostate Cancer Detection and Analysis using Advanced Machine Learning

Alzboon,

Al-Batah

2023

IJACSA

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Prostate cancer is one of the leading causes of cancer-related deaths among men. Early detection of prostate cancer is essential in improving the survival rate of patients. This study aimed to develop a machine-learning model for detecting and diagnosing prostate cancer using clinical and radiological data. The dataset consists of 200 patients with prostate cancer and 200 healthy controls and extracted features from their clinical and radiological data. Then, the data trained and evaluated using several machines learning models, including logistic Regression, decision tree, random forest, support vector machine, and neural network models, using 10-fold crossvalidation. Our results show that the random forest model achieved the highest accuracy of 0.92, with a sensitivity of 0.95 and a specificity of 0.89. The decision tree model achieved a nearly similar accuracy of 0.91, while the logistic regression, support vector machine, and neural network models achieved lower accuracies of 0.86, 0.87, and 0.88, respectively. Our findings suggest that machine learning models can effectively detect and diagnose prostate cancer using clinical and radiological data. The random forest model may be the most suitable model for this task.

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Section: Literature Reviewmentioning

confidence: 99%