Inception v3 hits 62% skin-cancer accuracy, beating GPT-6 Astra and Claude Fable 5.1

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If you are choosing a model for skin-cancer image classification, a task-specific CNN beat two frontier multimodal models in this evaluation. Inception v3 scored 62% on first-choice diagnosis, compared with 42% each for GPT-6 Astra and Claude Fable 5.1.

Here is what makes the comparison useful:

• The test used 200 biopsy-confirmed MILK10k lesions, each with clinical and dermoscopic images, and six possible diagnoses.
• Accepting second-choice diagnoses raised the scores to 173 cases for Inception v3, 134 for GPT-6 Astra, and 138 for Claude Fable 5.1.
• The CNN was fine-tuned on public ISIC images, with same-lesion and same-patient images excluded before training and no test-set tuning.
• Inception v3 can run in a mobile phone browser using WebGPU without uploading patient images.

One catch: this was a constrained, task-specific diagnostic test, not evidence of general clinical performance.

Sources: hyper.ai