ArticleBMC medical informatics and decision making2024
Improved prostate cancer diagnosis using a modified ResNet50-based deep learning architecture.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Prostate cancer detection using modified transformer with optimal feature selection from MRI images.Scientific reports · 2026Article
- [An Improved Faster R-CNN Method for Wound Detection].Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition · 2026Article
- Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.Nature communications · 2026Article
- MS-GATOR: multi-scale graph attention with topological reasoning for segmentation and classification of prostate cancer based on Gleason scores using histopathology images.Scientific reports · 2026Article
- Multimodal Deep Learning Based on Ultrasound Images and Clinical Data for Better Ovarian Cancer Diagnosis.Journal of imaging informatics in medicine · 2026Article
- Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.Current oncology (Toronto, Ont.) · 2026Review
- Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis.Journal of imaging · 2026Article
- Histopathology-Based Prostate Cancer Classification Using ResNet: A Comprehensive Deep Learning Analysis.Journal of imaging informatics in medicine · 2026Article
- Influence of high-performance image-to-image translation networks on clinical visual assessment and outcome prediction: utilizing ultrasound to MRI translation in prostate cancer.International journal of computer assisted radiology and surgery · 2026Article
- PRAD-Hybrid CNN (PRADHC): A Deep Learning Model for Assisted Diagnosis of Prostate Cancer on MRICurrent medical imaging · 2026Article
- A Multi-Stage Hybrid Learning Model with Advanced Feature Fusion for Enhanced Prostate Cancer Classification.Diagnostics (Basel, Switzerland) · 2025Article
- AI-driven prediction of severe respiratory sequelae in COVID-19 patients.Annals of medicine · 2025Article
- A dual enhanced stochastic gradient descent method with dynamic momentum and step size adaptation for improved optimization performance.Scientific reports · 2025Article
- Enhanced FISH Image Classification via CBAM-PPM-Optimized ResNet50 for Precision Cytogenetic Diagnosis.Sensors (Basel, Switzerland) · 2025Article
- Evaluation of Deep Learning Convolutional Neural Networks for Classification of Carcinoma Ex Pleomorphic Adenoma and Pleomorphic Adenoma in Whole-Slide Images.Head and neck pathology · 2025Article
- Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis.medRxiv : the preprint server for health sciences · 2025Article
- Deep Learning Techniques for Prostate Cancer Analysis and Detection: Survey of the State of the Art.Journal of imaging · 2025Review
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification.Scientific reports · 2025Article
- Lesion detection using artificial intelligence models in MR images of prostate cancer and prostatitis patients and comparison of model performance.Frontiers in urology · 2025Article
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4 authors.
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Abstract
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.