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

Healthcare
Computer vision
Mobile app development

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.

Request

GP2U Telehealth, an Australia-based GP online clinic, came up with the idea for the SkinView app. SkinView uses a disposable device that clips on to a smartphone and turns it into a digital dermatoscope. The application allows users to receive a skin cancer diagnosis without having to visit a doctor's office and pay the fee. When GP2U Telehealth turned to Integra Sources, the application was already under development. We were hired to port the app from Python to C++ and improve the quality of the computer vision algorithms.

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

Algorithms developed with the help of C++ have been wrapped by Objective-C and integrated into the iOS mobile application.
Objective-C has been used for iOS mobile application development.
C++ language in a compartment with OpenCV library has been used for algorithms converting from Python language.
Python language in combination with the scikit-image library has been used for receiving PoC.

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."

James Freeman,
CEO at GP2U Telehealth

Make some noise

Melanoma diagnostic accuracy

80%

Data processing time

< 0,1 seconds

Award-winning project at

Bytes4Health competition

Scope of work

Ported the code from Python to C++

Integrated the C++ code to the iOS application

Built unique algorithms for melanoma detection based on OpenCV

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