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Automated Prostate Cancer Detection on T2-Weighted MRI Using a Dual-Stream Attention Network: A Study on Private

Saeed Alqahtani^1,2, M A Jowhari^1,2, Yahya Q Sabi³

¹Radiological Sciences Department, College of Applied Medical Sciences, Najran University, Najran 61441, Saudi Arabia.

Journal of Clinical Medicine

|May 13, 2026

Summary

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Optimizing Prostate Imaging Practices in Saudi Arabian Hospitals: A Comprehensive Analysis of PI-RADS Compliance in Multiparametric MRI

Current medical imaging·2024

A new deep learning model automates prostate cancer risk classification from MRI scans, achieving high accuracy. This AI tool aids early detection and improves diagnostic efficiency for prostate cancer in Saudi Arabia.

Area of Science:

Radiology
Artificial Intelligence
Oncology

Background:

Prostate cancer incidence is rising in Saudi Arabia, necessitating improved early detection.
Accurate diagnosis via multiparametric MRI and PI-RADS scoring is hampered by radiologist experience dependency and limited subspecialists.
There is a critical need for advanced diagnostic tools to manage the growing prostate cancer burden.

Purpose of the Study:

To develop and validate a novel deep learning model for automated classification of prostate cancer lesions.
To differentiate between low-risk (PIRADS 2-3) and high-risk (PIRADS 4-5) prostate lesions using T2-weighted MRI.
To provide a standardized, high-precision diagnostic solution tailored for the Saudi Arabian population.

Main Methods:

A Dual-Stream Attention Network with a ResNet50 backbone was designed for lesion classification.

Keywords:

attention mechanism deep learning dual-stream network magnetic resonance imaging (MRI)medical image classification prostate cancer

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Parallel streams processed local and global features, enhanced by Channel-Spatial Attention modules.

Cross-Stream Fusion and Adaptive Feature Fusion integrated multi-scale information for optimized analysis.

Main Results:

The dual-stream attention network achieved 97.8% accuracy on the validation set and 96.4% on the test set.
The model demonstrated strong performance and generalization capabilities in classifying prostate lesions from Saudi patient data.
The AI model effectively distinguished between low-risk and high-risk prostate lesions.

Conclusions:

The proposed dual-stream architecture with novel attention and fusion mechanisms is highly effective for prostate cancer classification from T2-weighted MRI in Saudi clinical settings.
This study presents the first deep learning model specifically trained and validated on Saudi Arabian prostate MRI data.
The model has the potential to address the shortage of specialized expertise and enhance diagnostic efficiency for prostate cancer in the Kingdom.