الفهرس 
Chapter 1Only 14 pages are availabe for public view
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Abstract
In third-world countries, cervical cancer is considered one of prevalent and leading cause disease of death. It may be caused by a variety of factors, including smoking, poor nutritional status, immunological inadequacy, and prolonged use of contraception. The Pap Smear test, which is intended to prevent cervical cancer, finds preneoplastic changes in cervical epithelial cells. This study framework classified cervical cancer cells from Pap Smears into five specified cell types using machine learning-based classification algorithms. The SIPaKMeD database is used in this investigation. This public dataset, which was manually cropped by specialist from 966 cluster cell images taken from Pap Smear and produced 4045 isolated Pap Smear cells. Depending on their cellular form and structure, experts categorize the cells into five distinct types which are Superficial-intermediate, Parabasal, Koilocytotic, Dyskeratotic, and Metaplastic cells. Introducing a pipeline to improve algorithm accuracy and easy implementation by using pretrained CNN like inceptionV3 and pretrained feature extractor for extracting colors then conducting a suitable postprocess pipeline. The study reached the conclusion that machine learning like Support Vector Machine, Neural Network and K-Nearest Neighbor could improve Pap Smear screening classification results. The study concluded that the Support Vector Machine (SVM) is the most suitable algorithm for Pap Smear application because it trained on features extracted from pretrained CNN which focus on colors. The SVM has the highest accuracy of 98.5. These results support the proposed framework as a reliable classification diagnostic tool.