Preprocessing
Skin images need consistent preparation before a model can learn from them.
Deep-Learning Melanoma Detection
We developed computer-vision classification workflows for skin cancer detection, using CNN and ensemble models with transfer learning and demographic information to classify melanoma images.

What stood in the way.
Medical-image classification requires reliable preprocessing and transfer-learning models to produce useful results.
Skin images need consistent preparation before a model can learn from them.
Training from scratch is hard, so transfer learning from pretrained models was needed.
Demographic information can add context to what an image shows.
How we answered it.
We developed computer-vision classification workflows using CNN and ensemble approaches and transfer learning, combined with demographic information. Pretrained architectures such as ResNet and EfficientNet were part of the toolset, with OpenCV for image processing.
The parts that made the difference.
OpenCV is used to prepare images before classification.
Convolutional networks, combined in ensembles, classify the images.
Pretrained models are adapted to melanoma classification.
Demographic data is used alongside the image in classification.
What changed for the client.
The project delivered an automated research workflow for melanoma classification.
What it was built with.
Part of our AI and machine learning work.
The next big thing starts with a conversation.
An idea, a challenge, a “what if”. Tell us what you have in mind, and we’ll figure out the next step together.
What happens next
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