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Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture.

Fatma M Talaat1,2, Shaker El-Sappagh3,4, Khaled Alnowaiser5

  • 1Faculty of Artificial Intelligence, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt.

BMC Medical Informatics and Decision Making
|January 24, 2024
PubMed
Summary

This study introduces a Deep Learning (DL) model for prostate cancer detection, improving early diagnosis accuracy. The Prostate Cancer Detection Model (PCDM) shows high performance in identifying prostate cancer from medical images.

Keywords:
Convolution neural networkDual optimizerProstate cancer detectionResNet50

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Area of Science:

  • Oncology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Prostate cancer is the most common cancer in men, influenced by various risk factors.
  • Early detection improves patient outcomes, but balancing screening with overdiagnosis is challenging.
  • Deep Learning (DL) offers potential for accurate and efficient prostate cancer diagnosis, especially with difficult imaging.

Purpose of the Study:

  • To propose a Deep Learning (DL) model, the Prostate Cancer Detection Model (PCDM), for automatic prostate cancer diagnosis.
  • To evaluate the clinical applicability of the PCDM for early detection and management in healthcare settings.
  • To enhance prostate cancer detection accuracy and efficiency using advanced DL techniques.

Main Methods:

  • Developed a modified ResNet50-based architecture incorporating faster R-CNN and dual optimizers.
  • Trained the Prostate Cancer Detection Model (PCDM) on a large dataset of annotated medical images.
  • Compared the performance of the PCDM against standard ResNet50 and VGG19 architectures.

Main Results:

  • The proposed PCDM model demonstrated superior performance compared to ResNet50 and VGG19.
  • Achieved high diagnostic metrics: 97.40% sensitivity, 97.09% specificity, 97.56% precision, and 95.24% accuracy.
  • The model's architecture and training strategy significantly improved detection capabilities.

Conclusions:

  • The Prostate Cancer Detection Model (PCDM) is a clinically applicable tool for aiding in the early detection of prostate cancer.
  • DL algorithms, like the proposed PCDM, can significantly enhance the accuracy and efficiency of prostate cancer diagnosis.
  • The model's high performance suggests its potential for real-world healthcare integration to improve patient management.