Skincure: An Innovative Smart Phone-Based Application To Assist In Melanoma Early Detection And Prevention

TL;DR

Skincure is a smartphone application designed to aid early melanoma detection and prevention through real-time alerts and automated image analysis, achieving high accuracy in classifying skin lesions.

Contribution

The paper introduces a novel smartphone app with real-time sunburn risk alerts and an automated dermoscopy image analysis system for melanoma detection.

Findings

Achieved over 96% accuracy in classifying melanoma and normal skin images.

Developed a new equation for estimating skin burn time.

Demonstrated system efficiency with high classification accuracy.

Abstract

Melanoma spreads through metastasis, and therefore it has been proven to be very fatal. Statistical evidence has revealed that the majority of deaths resulting from skin cancer are as a result of melanoma. Further investigations have shown that the survival rates in patients depend on the stage of the infection; early detection and intervention of melanoma implicates higher chances of cure. Clinical diagnosis and prognosis of melanoma is challenging since the processes are prone to misdiagnosis and inaccuracies due to doctors subjectivity. This paper proposes an innovative and fully functional smart-phone based application to assist in melanoma early detection and prevention. The application has two major components; the first component is a real-time alert to help users prevent skin burn caused by sunlight; a novel equation to compute the time for skin to burn is thereby introduced. The second component is an automated image analysis module which contains image acquisition, hair detection and exclusion, lesion segmentation, feature extraction, and classification. The proposed system exploits PH2 Dermoscopy image database from Pedro Hispano Hospital for development and testing purposes. The image database contains a total of 200 dermoscopy images of lesions, including normal, atypical, and melanoma cases. The experimental results show that the proposed system is efficient, achieving classification of the normal, atypical and melanoma images with accuracy of 96.3%, 95.7% and 97.5%, respectively.