Abstract :

Cervical cancer is one of the most common cancers affecting women worldwide and early diagnosis significantly improves treatment outcomes. Traditional Pap smear tests require skilled cytotechnologists and pathologists to manually examine cell samples under a microscope, which can be time-consuming and prone to human errors. Computer Vision (CV)-based diagnostic systems harness image processing and machine learning to automate the detection of abnormal cells from Pap smear images. These systems analyze cell morphology, texture, and staining patterns to distinguish between normal, precancerous, and cancerous cells. By integrating with microscope imaging, the CV system enhances accuracy and reduces diagnostic time. Deep learning models such as Convolutional Neural Networks (CNN) have demonstrated superior performance in feature extraction and classification. The proposed diagnostic framework uses CV algorithms to segment, preprocess, and classify cervical cells. Real-time analysis enables