SynthesisJournal of cellular and molecular medicine2024
A systematic review on deep learning-based automated cancer diagnosis models.
Synthesis in Journal of cellular and molecular medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews.Journal of medical Internet research · 2025Pooled it
- A systematic review on deep learning-based automated cancer diagnosis models.Journal of cellular and molecular medicine · 2024Pooled it
- In silico models in oncology, neurology, and epidemiology: systems-level and multiscale perspectives.NPJ systems biology and applications · 2026Review
- CT-Based Glioma Segmentation Using Deep Learning: Validation for Emergency Neuro-oncological Care.Journal of imaging informatics in medicine · 2026Article
- Browser-Based Multi-Cancer Classification Framework Using Depthwise Separable Convolutions for Precision Diagnostics.Diagnostics (Basel, Switzerland) · 2025Article
- Development and multi-institutional validation of the Swin transformer model for prediction of spontaneous passage of ureteral calculi: A retrospective multicentric study.Urolithiasis · 2025Article
- A promptable CT foundation model for solid tumor evaluation.NPJ precision oncology · 2025Article
- SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.Current medical imaging · 2025Article
- Diagnostic Accuracy of Deep Learning for Predicting Lymph Node Metastasis Based on Computed Tomography in Gastric Cancer: A Systematic Review and Meta-analysis.Medical journal of the Islamic Republic of Iran · 2025Review
- A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer.Frontiers in oncology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is gaining importance due to its wide range of applications. Many researchers have utilized deep learning (DL) models for the automated diagnosis of cancer patients. This paper provides a systematic review of DL models for automated diagnosis of cancer patients. Initially, various DL models for cancer diagnosis are presented. Five major categories of cancers such as breast, lung, liver, brain and cervical cancer are considered. As these categories of cancers have a very high percentage of occurrences with high mortality rate. The comparative analysis of different types of DL models is drawn for the diagnosis of cancer at early stages by considering the latest research articles from 2016 to 2022. After comprehensive comparative analysis, it is found that most of the researchers achieved appreciable accuracy with implementation of the convolutional neural network model. These utilized the pretrained models for automated diagnosis of cancer patients. Various shortcomings with the existing DL-based automated cancer diagnosis models are also been presented. Finally, future directions are discussed to facilitate further research for automated diagnosis of cancer patients.
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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.