(molecheck.info)

422 points sungam | 2 comments | 07 Sep 25 10:38 UTC | HN request time: 0.498s | source

Coded using Gemini Pro 2.5 (free version) in about 2-3 hours.

Single file including all html/js/css, Vanilla JS, no backend, scores persisted with localStorage.

Deployed using ubuntu/apache2/python/flask on a £5 Digital Ocean server (but could have been hosted on a static hosting provider as it's just a single page with no backend).

Images / metadata stored in an AWS S3 bucket.

1. cjbgkagh ◴[07 Sep 25 16:08 UTC] No.45159422[source]▶

>>45157020 (OP) #

This is great, I had no idea how off base I was with my assumptions. It’ll be interesting to keep the usage data to find out what kinds of images people have the most trouble with. As in what kind of mole is the most likely to be missed. Though perhaps dermatologist already know that answer well enough.

I would love to see more of such classifiers for other medical conditions, googling for images tends not to produce a representative sample.

replies(1): >>45159550 #

2. sungam ◴[07 Sep 25 16:20 UTC] No.45159550[source]▶

>>45159422 (TP) #

Thanks, I'm really pleased that people have found it useful! Wasn't expecting much from the app just coded it in an evening as it's something I've been thinking about for years

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Show HN: I'm a dermatologist and I vibe coded a skin cancer learning app