Cascade Classification of Dermoscopic Images of Skin Neoplasms with Controllable Sensitivity and External Clinical Validation
arXiv:2606.13135v1 Announce Type: cross Abstract: Purpose. To compare deep learning architectures and classification schemes for dermoscopic images of skin neoplasms and assess their generalization on transfer from open international datasets to independent clinical datasets of Russian practice. Methods. Four architectures (ViT-B/16, Swin-S, ConvNeXt-S, EfficientNetV2-S) were compared in three schemes: binary (malignant/benign), single-stage four-class (benign, MEL, SCC, BCC), and a two-stage cascade (binary triage, then three-class differentiation MEL/SCC/BCC). All models used ImageNet-pretra
The proliferation of advanced AI techniques and the accessibility of large medical datasets are enabling precise applications in diagnostics, addressing long-standing clinical challenges.
This development indicates a tangible advancement in AI-driven medical diagnostics, potentially improving early detection and treatment outcomes for skin neoplasms globally.
AI models are becoming more sophisticated in medical image analysis, demonstrating validated performance on diverse clinical data, which is crucial for real-world adoption.
- · MedTech AI companies
- · Dermatologists
- · Patients with skin conditions
- · Healthcare systems
- · Traditional diagnostic methods
- · Medical AI companies with less robust validation
Increased accuracy and efficiency in diagnosing skin cancer, reducing diagnostic delays and errors.
Integration of these AI tools into routine clinical practice, potentially lowering healthcare costs and increasing access to specialized diagnostics.
The establishment of new regulatory frameworks and ethical guidelines specifically for AI applications in medical diagnosis, driven by widespread adoption.
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