Machine Learning for Uterine Cervix Screening
- Resource Type
- Conference
- Authors
- Mercaldo, Francesco; Zhou, Xiaoli; Huang, Pan; Martinelli, Fabio; Santone, Antonella
- Source
- 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE) BIBE Bioinformatics and Bioengineering (BIBE), 2022 IEEE 22nd International Conference on. :71-74 Nov, 2022
- Subject
- Bioengineering
Computing and Processing
Signal Processing and Analysis
Pregnancy
Embryo
Microscopy
Machine learning
Biological systems
Feature extraction
Numerical models
cervix
cancer
screening
machine learning
artificial intelligence
- Language
- ISSN
- 2471-7819
Cervical cancer develops in the lower part of the uterus, the organ of the female apparatus where the embryo is received and develops during pregnancy. In this paper we investigate the possibility to automatically detect the presence of cancerous cells and to predict of the stage of the cancerous lesion of the uterine cervix by exploiting images of cervical cells captured by the microscope. We extract a set of numerical features from each images and we build supervised machine learning models to diagnose the cervix cancer. The experimental analysis show that the proposed method is promising in distinguish between healthy and cancerous cells and to detect also high and low-grade squamous intraepithelial lesions.