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Related Experiment Video

Updated: Nov 3, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

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Published on: March 21, 2025

Precise Identification of Prostate Cancer from DWI Using Transfer Learning.

Islam R Abdelmaksoud^1,2, Ahmed Shalaby¹, Ali Mahmoud¹

¹Bioengineering Department, University of Louisville, Louisville, KY 40292, USA.

Sensors (Basel, Switzerland)

|June 2, 2021

Summary

This summary is machine-generated.

This study developed a computer-aided detection (CAD) system using diffusion-weighted imaging (DWI) and deep learning to accurately detect prostate cancer. The system achieved high accuracy, demonstrating its potential to improve non-invasive cancer diagnosis.

Keywords:

ADC maps ALexNet VGGNet prostate cancer transfer learning

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Area of Science:

Medical Imaging
Artificial Intelligence in Oncology
Radiology

Background:

Computer-aided detection (CAD) systems enhance radiologist objectivity and reduce reliance on invasive procedures.
Prostate cancer detection can be improved through advanced imaging analysis.
Diffusion-weighted imaging (DWI) offers valuable insights into tissue characteristics.

Purpose of the Study:

To develop and evaluate a CAD system for detecting and identifying prostate cancer using DWI.
To assess the performance of deep learning models in analyzing DWI data for prostate cancer diagnosis.

Main Methods:

Non-negative matrix factorization (NMF) for prostate region segmentation.
Apparent diffusion coefficient (ADC) volume estimation and radiologist-based labeling.
Transfer learning with fine-tuned convolutional neural network (CNN) models (AlexNet, VGGNet) for classification.

Main Results:

Experiments evaluated CNN models on DWI datasets with nine b-values.
AlexNet achieved an average accuracy of 89.2±1.5%, with sensitivity 87.5±2.3% and specificity 90.9±1.9%.
VGGNet demonstrated improved performance with an average accuracy of 91.2±1.3%, sensitivity 91.7±1.7%, and specificity 90.1±2.8%.

Conclusions:

The developed CAD system is feasible and accurate for prostate cancer detection using DWI.
Deeper CNN models like VGGNet significantly improve detection accuracy.
The system shows promise for enhancing non-invasive prostate cancer diagnosis.