The Development and Performance of a Machine Learning Based Mobile Platform for Visually Determining the Etiology of Penile Pathology

arXiv:2403.08417v1 Announce Type: cross
Abstract: Machine-learning algorithms can facilitate low-cost, user-guided visual diagnostic platforms for addressing disparities in access to sexual health services. We developed a clinical image dataset using original and augmented images for five penile diseases: herpes eruption, syphilitic chancres, penile candidiasis, penile cancer, and genital warts. We used a U-net architecture model for semantic pixel segmentation into background or subject image, the Inception-ResNet version 2 neural architecture to classify each pixel as diseased or non-diseased, and a …

abstract algorithms arxiv clinical cost cs.cv cs.lg dataset development diagnostic diseases eess.iv five health image images low machine machine learning mobile pathology performance platform platforms services type visual