ArticleCancers2023
Deep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Organ Segmentation with Machine Learning Models.Journal of imaging · 2026Article
- Machine learning algorithms and web-based prognostic tool for different histological subtypes of osteosarcoma: a retrospective cohort.Annals of medicine and surgery (2012) · 2026Article
- Enhancing bone cancer detection through optimized pre trained deep learning models and explainable AI using the osteosarcoma tumor assessment dataset.Scientific reports · 2025Article
- Comparative Analysis of Deep Neural Networks for Automated Ulcerative Colitis Severity Assessment.Bioengineering (Basel, Switzerland) · 2025Article
- Drug Repurposing: A Conduit to Unravelling Metabolic Reprogramming for Cancer Treatment.Mini reviews in medicinal chemistry · 2025Review
- Advanced hybrid deep learning model for enhanced evaluation of osteosarcoma histopathology images.Frontiers in medicine · 2025Article
- Whole-Exome Analysis and Osteosarcoma: A Game Still Open.International journal of molecular sciences · 2024Review
- AI based diagnostics product design for osteosarcoma cells microscopy imaging of bone cancer patients using CA-MobileNet V3.Journal of bone oncology · 2024Article
- Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Detecting Skin Reactions in Epicutaneous Patch Testing with Deep Learning: An Evaluation of Pre-Processing and Modality Performance.Bioengineering (Basel, Switzerland) · 2023Article
- A Novel Hybrid Approach for Classifying Osteosarcoma Using Deep Feature Extraction and Multilayer Perceptron.Diagnostics (Basel, Switzerland) · 2023Article
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3 authors.
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Abstract
backgroundOsteosarcoma is the most common primary malignancy of the bone, being most prevalent in childhood and adolescence. Despite recent progress in diagnostic methods, histopathology remains the gold standard for disease staging and therapy decisions. Machine learning and deep learning methods have shown potential for evaluating and classifying histopathological cross-sections.
methodsThis study used publicly available images of osteosarcoma cross-sections to analyze and compare the performance of state-of-the-art deep neural networks for histopathological evaluation of osteosarcomas.
resultsThe classification performance did not necessarily improve when using larger networks on our dataset. In fact, the smallest network combined with the smallest image input size achieved the best overall performance. When trained using 5-fold cross-validation, the MobileNetV2 network achieved 91% overall accuracy.
conclusionsThe present study highlights the importance of careful selection of network and input image size. Our results indicate that a larger number of parameters is not always better, and the best results can be achieved on smaller and more efficient networks. The identification of an optimal network and training configuration could greatly improve the accuracy of osteosarcoma diagnoses and ultimately lead to better disease outcomes for 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.