ArticleCancer biomarkers : section A of Disease markers2018
Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies.
Article in Cancer biomarkers : section A of Disease markers, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Advancing early detection of chromophobe renal cell carcinoma: a Bayesian optimization approach to machine learning models.Scientific reports · 2026Article
- Vision Transformer Autoencoders for Unsupervised Representation Learning: Revealing Novel Genetic Associations through Learned Sparse Attention Patterns.medRxiv : the preprint server for health sciences · 2025Article
- Article
- Machine learning-based integration develops a mitophagy-related lncRNA signature for predicting the progression of prostate cancer: a bioinformatic analysis.Discover oncology · 2024Article
- Enhancing lung cancer detection through hybrid features and machine learning hyperparameters optimization techniques.Heliyon · 2024Article
- Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning.Prostate cancer · 2024Review
- Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique.PloS one · 2024Article
- A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application.BMC bioinformatics · 2023Review
- Review
- Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI.Scientific reports · 2022Article
- A New Framework for Precise Identification of Prostatic Adenocarcinoma.Sensors (Basel, Switzerland) · 2022Article
- Imaging of Neuroendocrine Prostatic Carcinoma.Cancers · 2021Review
- Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response.Biomedical engineering online · 2021Article
- Radiomic features for prostate cancer grade detection through formal verification.La Radiologia medica · 2021Article
- Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives.Diagnostics (Basel, Switzerland) · 2021Review
- Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.Biomedical engineering online · 2020Article
- Radiomics for Gleason Score Detection through Deep Learning.Sensors (Basel, Switzerland) · 2020Article
- Machine Learning-Assisted Evaluation of Circulating DNA Quantitative Analysis for Cancer Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2020Article
- Detecting prostate cancer using deep learning convolution neural network with transfer learning approach.Cognitive neurodynamics · 2020Article
- Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model.American journal of cancer research · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate is a second leading causes of cancer deaths among men. Early detection of cancer can effectively reduce the rate of mortality caused by Prostate cancer. Due to high and multiresolution of MRIs from prostate cancer require a proper diagnostic systems and tools. In the past researchers developed Computer aided diagnosis (CAD) systems that help the radiologist to detect the abnormalities. In this research paper, we have employed novel Machine learning techniques such as Bayesian approach, Support vector machine (SVM) kernels: polynomial, radial base function (RBF) and Gaussian and Decision Tree for detecting prostate cancer. Moreover, different features extracting strategies are proposed to improve the detection performance. The features extracting strategies are based on texture, morphological, scale invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) features. The performance was evaluated based on single as well as combination of features using Machine Learning Classification techniques. The Cross validation (Jack-knife k-fold) was performed and performance was evaluated in term of receiver operating curve (ROC) and specificity, sensitivity, Positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR). Based on single features extracting strategies, SVM Gaussian Kernel gives the highest accuracy of 98.34% with AUC of 0.999. While, using combination of features extracting strategies, SVM Gaussian kernel with texture + morphological, and EFDs + morphological features give the highest accuracy of 99.71% and AUC of 1.00.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.