Machine Vision Techniques for Cervical Intraepithelial Neoplasia Detection

Cervical intraepithelial neoplasia (CIN) is a precancerous condition of the cervix that, if left untreated, can develop into cervical cancer. Preventing cervical cancer requires the early detection and management of CIN. Machine vision techniques have the potential to greatly enhance CIN-C diagnosis and treatment. These methods can be used to examine digital images of cervical cells and determine whether any aberrant cells are present based on characteristics like size, shape, colour, and texture. Additionally, patterns in cervical cytology samples that are suggestive of CIN can be recognized by machine learning algorithms. The accuracy and specificity of CIN-C diagnosis can also be increased by combining machine vision with other technologies like optical coherence tomography and spectroscopy. Additionally, the development of portable and low-cost cervical cancer screening systems can increase access to healthcare in resource-limited settings. This paper reviews the current state of the art in machine vision techniques for CIN-C detection, highlighting recent developments and future perspectives in this field.