Detection and Classification of Hysteroscopic Images Using Deep Learning.

Diego RaimondoAntonio RaffonePaolo SalucciIvano RaimondoGiampiero CapobiancoFederico Andrea GalatoloMario Giovanni Cosimo Antonio CiminoAntonio TravaglinoManuela MalettaStefano Ferla Agnese VirgilioDaniele NeolaPaolo Casadio Renato Seracchioli

Published in: Cancers (2024)

Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.

Keyphrases

deep learning
convolutional neural network
artificial intelligence
machine learning
loop mediated isothermal amplification
label free
real time pcr
big data
electronic health record
optical coherence tomography