ArticleScientific reports2024
Automating cancer diagnosis using advanced deep learning techniques for multi-cancer image classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning models for ICU readmission prediction: a systematic review and meta-analysis.Critical care (London, England) · 2025Pooled it
- Decoding Glioblastoma Complexity Through Extracellular Vesicles, Organ-on-Chip Models, and Deep Learning.Cells · 2026Review
- Targeting extrachromosomal DNA in human cancers.Nature reviews. Drug discovery · 2026Review
- Robust Histopathology Subtyping via Perturbation Fidelity in Deep Classifier.Journal of imaging informatics in medicine · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Advanced breath analysis through hierarchical deep convolutional neural network for multi-cancer screening.NPJ digital medicine · 2026Article
- An optimized bidirectional recurrent neural network for kidney stone detection based on developed bald eagle search method in CT scan images.Scientific reports · 2025Article
- Evidential deep learning-based ALK-expression screening using H&E-stained histopathological images.NPJ digital medicine · 2025Article
- Integrating tumor location into artificial intelligence-based prognostic models in cancer.World journal of clinical oncology · 2025Article
- The dark matter in cancer immunology: beyond the visible- unveiling multiomics pathways to breakthrough therapies.Journal of translational medicine · 2025Review
- Deep learning and inflammatory markers predict early response to immunotherapy in unresectable NSCLC: A multicenter study.Biomolecules & biomedicine · 2025Article
- Article
- Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions.Biomedicines · 2025Review
- Transforming cervical cancer pathological diagnosis through artificial intelligence: progress, performance, and barriers to clinical implementation.Frontiers in oncology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Cancer detection poses a significant challenge for researchers and clinical experts due to its status as the leading cause of global mortality. Early detection is crucial, but traditional cancer detection methods often rely on invasive procedures and time-consuming analyses, creating a demand for more efficient and accurate solutions. This paper addresses these challenges by utilizing automated cancer detection through AI-based techniques, specifically focusing on deep learning models. Convolutional Neural Networks (CNNs), including DenseNet121, DenseNet201, Xception, InceptionV3, MobileNetV2, NASNetLarge, NASNetMobile, InceptionResNetV2, VGG19, and ResNet152V2, are evaluated on image datasets for seven types of cancer: brain, oral, breast, kidney, Acute Lymphocytic Leukemia, lung and colon, and cervical cancer. Initially, images undergo segmentation techniques, proceeded by contour feature extraction where parameters such as perimeter, area, and epsilon are computed. The models are rigorously evaluated, with DenseNet121 achieving the highest validation accuracy as 99.94%, 0.0017 as loss, and the lowest Root Mean Square Error (RMSE) values as 0.036056 for training and 0.045826 for validation. These results revealed the capability of AI-based techniques in improving cancer detection accuracy, with DenseNet121 emerging as the most effective model in this study.
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Registered trials
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.