In terms of Medical Informatics the Machine Learning (ML) (and Deep Learning as its post-modern form) is one the most attractive part of the Artificial Intelligence (AI) that makes possible to teach extremely complicated algorithms to machines easily. But it offers a more interesting dimension: the transferring learning to another machine/software runs on different environment.
Today in this post I’ll share you a health informatic application more specifically a dermatologic diagnosis application that I developed and presented at one of my PhD course.
Final objective: a web page using device cam for getting skin photos, then makes prediction for malign/benign.
To achieve this goal, all I need is an image classification algorithm. I’ll train it then transfer the learning to my web page. The training should run on a ML library and this learning (data files) should be able to transfer to a web page environment (HTML + JS).
I got the training data from the ISIC (International Skin Imaging Collaboration) images for training (ML). ISIC has around 70K images with their metadata (deidentified EHR). It was too deep for my pilot study that’s why I’ve filtered them by ages and diagnosis finally got just 260 malign + 300 benign images.
I added two classes named Benign and Malign and put images as samples of those two class according to their diagnosis.
You can see the Advanced menu at the footer of the training block shows advanced settings. You can see more detailed results and charts by clicking the “under the hood” item.
Well, the results are not bad at all. But by this point I skip improving the accuracy of the model to save time. I’ll prefer to improve it by using ts.js API at the next phase because there are only a few parameters on UI.
The next step is the transferring.
The teachable machine has a easy way to download training model data files that we called it the “learning” in this post.
The teachable machine provides two main options to save model (learning). The first is upload it to the teachable machine’s own platform and the second is download it to your own page.
After the downloading model files and the HTML code, all you need to do is to upload it to your website.
After downloading and uploading it to my web space, I made a few modifications on the code. In next phase I’ll add an upload button instead of the cam. Uploading them directly to the Teachable Machine is a better choice If you do not have a web space. It is free. I used my own.
I also uploaded it to the TM can be reached at https://teachablemachine.withgoogle.com/models/uaL1JHYtX/
Currently the accuracy is not enough to qualify to use it for clinical purposes. But it is ok for just a homework study. I’ll try improve the sensitivity in next phase. I’ll also apply some usability testes to design a better UI/UX.
The ML and model transferring concept can open wonderful opportunities for medical informatics, like developing DSS (decision support system) or mobile applications. Simple ML models can replace the chaotic algorithms with efficient products. ML apps are widely developing for the health fields using image processing or classification like radiology and dermatology.