SkinView iOS App for Identifying Melanoma Skin Cancer Using Computer Vision Algorithms

Healthcare

Computer vision

Mobile app development

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About

A mole is a benign (non-cancerous) skin tumor. Almost everyone has from 30 to 60 moles on their body. And nearly all of these moles are harmless. But some types of moles are slightly more likely to develop into melanoma than other types of moles. If a mole has the characteristics of the ABCDEs (Asymmetry, Border, Color, Diameter, and Evolution) of melanoma it should be checked by a dermatologist.

Solution

We ported the SkinView app to C++ but our collaboration with GP2U didn't stop there. Our client was so impressed with the quality of our work and development approach that he decided to continue working with us on enhancing the accuracy of melanoma detection and optimizing the iOS app performance.

Technologies Used

Python language in combination with the scikit-image library has been used for receiving PoC.

C++ language in a compartment with OpenCV library has been used for algorithms converting from Python language.

Objective-C has been used for iOS mobile application development.

Algorithms developed with the help of C++ have been wrapped by Objective-C and integrated into the iOS mobile application.

Results

We significantly improved the algorithms for image processing and recognition. The melanoma diagnostic accuracy of our algorithms increased from 30% to 80%. Despite the complexity of the algorithms, we managed to decrease the processing time of the data to less than 0,1 second while the original version of the app required 4-6 seconds to process the image and show results.

SkinView became one of the two winners of the Murdoch Childrens Research Institute Bytes4Health competition in November 2016. We continued working with the GP2U Telehealth company on another project.

"Integra Sources are great to work with and highly skilled. Definitely A graders."