Prostate Cancer Detection, Segmentation, and Classification using Deep Neural Networks

Yahia Bouslimi, Takwa Gader, Afef Echi

2023

Abstract

This paper provides a fully automated computer-aided medical diagnostic system that assists radiologists in segmenting Prostate Cancer (PCa) Lesions from multi-parametric Magnetic Resonance Imaging (mp-MRIs) and predicting whether those lesions are benign or malignant. For that, our proposed approach used deep learning neural networks models such as residual networks (ResNet) and inception networks to classify clinically relevant cancer. It also used U-Net and MultiResU-Net to automatically segment the prostate lesion from mp-MRI’s. We used two publicly available benchmark datasets: the Radboudumc and ProstateX. We tested our fully automatic system and obtained positive findings, with the AUROC of the PCa lesion classification model exceeding 98.4% accuracy. On the other hand, the MultiResU-Net model achieved an accuracy of 98.34% for PCa lesion segmentation.

Paper Citation

in Harvard Style

Bouslimi Y., Gader T. and Echi A. (2023). Prostate Cancer Detection, Segmentation, and Classification using Deep Neural Networks. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-626-2, pages 534-541. DOI: 10.5220/0011795100003411

in Bibtex Style

@conference{icpram23,
author={Yahia Bouslimi and Takwa Gader and Afef Echi},
title={Prostate Cancer Detection, Segmentation, and Classification using Deep Neural Networks},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2023},
pages={534-541},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011795100003411},
isbn={978-989-758-626-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Prostate Cancer Detection, Segmentation, and Classification using Deep Neural Networks
SN - 978-989-758-626-2
AU - Bouslimi Y.
AU - Gader T.
AU - Echi A.
PY - 2023
SP - 534
EP - 541
DO - 10.5220/0011795100003411