AI and machine learning (ML) techniques used to identify alterations in human genome correlating in prostate cancer (PCa), usefulness radiotherapy and in prostate cancer surgery. Genomics Article/Reference

Deep learning models help in diagnosing and treating urological conditions and have proved their ability to detect prostate cancer, bladder tumors, renal cell carcinoma, along with ultrasound image analysis. A general schematic diagram of DL models can be seen in Figure 1.

In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes).

Sep 19, 2023 · This study wholistically investigated the application of machine learning and deep learning models in prostate cancer detection and diagnosis. We also conducted a publisher-based comparison to give readers a view of some possible tendencies such as the potential impact.

Workflow of machine learning (ML) and deep learning (DL) based multi-class lesion classification for prostate cancer using an axial T2-weighted sequence. The workflow follows a similar...

Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574).

The results were compared with hand-crafted features such as texture, morphology, and gray level co-occurrence matrix (GLCM) using non-deep learning classifiers such as support vector machine (SVM) Gaussian Kernel, k-nearest neighbor-Cosine (KNN - Cosine), kernel naive Bayes, decision tree (DT) and RUSBoost tree.

Jan 1, 2021 · Automated deep learning systems have delivered promising results from histopathological images to accurate grading of prostate cancer. Many studies have shown that deep learning strategies can achieve better outcomes than simpler systems that make use of pathology samples.

Dec 1, 2024 · Building an accurate model from electronic data using machine learning is quite difficult. This review article presents a systematic literature review focused on the area of prostate cancer survival analysis utilizing machine learning and other soft computing techniques.

Multiparametric (mp) magnetic resonance imaging (MRI) represents a robust tool for detecting prostate cancers (PCa). However, its interpretation requires skille.