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

Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) alg...

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Vydáno v:BMC medical informatics and decision making Ročník 24; číslo 1; s. 23 - 14
Hlavní autoři: Talaat, Fatma M., El-Sappagh, Shaker, Alnowaiser, Khaled, Hassan, Esraa
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 24.01.2024
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Accuracy
Algorithms
Analysis
Biopsy
Cancer
Cancer therapies
Convolution neural network
Datasets
Deep Learning
Diagnosis
Dual optimizer
Efficiency
Genetics
Health Facilities
Health Informatics
Humans
Information Systems and Communication Service
Innovations
Literature reviews
Machine learning
Male
Management of Computing and Information Systems
Medical diagnosis
Medical imaging
Medicine
Medicine & Public Health
Methods
Neural networks
Prostate
Prostate cancer
Prostate cancer detection
Prostatic Neoplasms - diagnostic imaging
ResNet50
Tumors
Egypt
Prostate cancer detection
Dual optimizer
Convolution neural network
ResNet50
ISSN:1472-6947, 1472-6947
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Popis
Shrnutí:Prostate cancer, the most common cancer in men, is influenced by age, family history, genetics, and lifestyle factors. Early detection of prostate cancer using screening methods improves outcomes, but the balance between overdiagnosis and early detection remains debated. Using Deep Learning (DL) algorithms for prostate cancer detection offers a promising solution for accurate and efficient diagnosis, particularly in cases where prostate imaging is challenging. In this paper, we propose a Prostate Cancer Detection Model (PCDM) model for the automatic diagnosis of prostate cancer. It proves its clinical applicability to aid in the early detection and management of prostate cancer in real-world healthcare environments. The PCDM model is a modified ResNet50-based architecture that integrates faster R-CNN and dual optimizers to improve the performance of the detection process. The model is trained on a large dataset of annotated medical images, and the experimental results show that the proposed model outperforms both ResNet50 and VGG19 architectures. Specifically, the proposed model achieves high sensitivity, specificity, precision, and accuracy rates of 97.40%, 97.09%, 97.56%, and 95.24%, respectively.
Bibliografie:ObjectType-Article-1
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ISSN:1472-6947
1472-6947
DOI:10.1186/s12911-024-02419-0