Melatect: A Machine Learning Model Approach For Identifying Malignant Melanoma in Skin Growths
This provides a convenient tool for users to get free advice on skin lesions, though it appears incremental as it builds on existing datasets and methods.
The paper tackles the problem of identifying malignant melanoma in skin lesions by presenting Melatect, a machine learning model embedded in an iOS app that achieves over 96.6% accuracy in classification.
Malignant melanoma is a common skin cancer that is mostly curable before metastasis -when growths spawn in organs away from the original site. Melanoma is the most dangerous type of skin cancer if left untreated due to the high risk of metastasis. This paper presents Melatect, a machine learning (ML) model embedded in an iOS app that identifies potential malignant melanoma. Melatect accurately classifies lesions as malignant or benign over 96.6% of the time with no apparent bias or overfitting. Using the Melatect app, users have the ability to take pictures of skin lesions (moles) and subsequently receive a mole classification. The Melatect app provides a convenient way to get free advice on lesions and track these lesions over time. A recursive computer image analysis algorithm and modified MLOps pipeline was developed to create a model that performs at a higher accuracy than existing models. Our training dataset included 18,400 images of benign and malignant lesions, including 18,000 from the International Skin Imaging Collaboration (ISIC) archive, as well as 400 images gathered from local dermatologists; these images were augmented using DeepAugment, an AutoML tool, to 54,054 images.