  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.
