A new deep learning model combining dermoscopic images with patient metadata achieves 94.5% accuracy in melanoma detection.
Key Details
- 1AI model integrates dermoscopic images and patient data (age, gender, lesion site) for diagnosis.
- 2Achieved 94.5% accuracy and an F1-score of 0.94 using the SIIM-ISIC dataset of over 33,000 images.
- 3Outperformed popular image-only AI models like ResNet-50 and EfficientNet.
- 4Feature analysis showed lesion size, age, and site significantly impact diagnostic accuracy.
- 5Model developed by international team led by Incheon National University; to be published in Information Fusion (Dec 2025).
- 6Potential applications include smartphone-based diagnosis, telemedicine, and AI-assisted dermatology clinics.
Why It Matters
This study demonstrates the value of multimodal AI in diagnostic imaging, highlighting better performance and transparency in early skin cancer detection. Such advances can inform future imaging-AI integration strategies, with implications for clinical workflow and patient care in dermatology and radiology.
Source
EurekAlert
Related News
•EurekAlert
Deep Learning AI Outperforms Clinic Prognostics for Colorectal Cancer Recurrence
A new deep learning model using histopathology images identifies recurrence risk in stage II colorectal cancer more effectively than standard clinical predictors.
•EurekAlert
AI Reveals Key Health System Levers for Cancer Outcomes Globally
AI-based analysis identifies the most impactful policy and resource factors for improving cancer survival across 185 countries.
•EurekAlert
Dual-Branch Graph Attention Network Predicts ECT Success in Teen Depression
Researchers developed a dual-branch graph attention network that uses structural and functional MRI data to accurately predict individual responses to electroconvulsive therapy in adolescents with major depressive disorder.