At Skinive we want to raise awareness of fight against skin diseases and provide people with simple&affordable solution for their early detection.
Skinive helps users monitor skin's health by analyzing images of moles, rash, spots and other issues.
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A very cool application that can really help people especially in 21 studies when time and health come first
Feedback: you need to have a button to flip the camera i.e. checking something on your face, neck, shoulder etc. is easier doing selfie mode.
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Great job, guys!
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Idea looks amazing! How accurate is the AI in identifying different skin conditions? What kind of data was it trained on, and how diverse was the dataset?
Our algorithm has over 90% accuracy and has been trained on a propietary dataset with a diverse dataset. More details about the dataset, error matrix, etc. can be found in the document ‘accuracy report’ - https://skinive.com/skinive-accuracy2022/
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Really interesting approach to making skin health monitoring accessible. The AI-powered analysis from just a photo is impressive — I'm curious how you handle the accuracy challenge, especially across different skin tones and lighting conditions? I'm building something similar in the food space (scanning ingredient lists for allergens and health risks) and the trust/accuracy balance is one of the hardest parts to get right. Congrats on the 4.9 rating with 42 reviews — that's solid validation!
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Skinive AI: Skin Scanner, health checkup
The Tech Rooport
Idea looks amazing! How accurate is the AI in identifying different skin conditions? What kind of data was it trained on, and how diverse was the dataset?
Skinive AI: Skin Scanner, health checkup
@alex_seletsky
Our algorithm has over 90% accuracy and has been trained on a propietary dataset with a diverse dataset. More details about the dataset, error matrix, etc. can be found in the document ‘accuracy report’ - https://skinive.com/skinive-accuracy2022/
Really interesting approach to making skin health monitoring accessible. The AI-powered analysis from just a photo is impressive — I'm curious how you handle the accuracy challenge, especially across different skin tones and lighting conditions? I'm building something similar in the food space (scanning ingredient lists for allergens and health risks) and the trust/accuracy balance is one of the hardest parts to get right. Congrats on the 4.9 rating with 42 reviews — that's solid validation!