Cervical Cell Classification using Machine Learning

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dc.contributor.advisorTóth, János
dc.contributor.authorAli, Azan
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2025-02-22T20:49:07Z
dc.date.available2025-02-22T20:49:07Z
dc.date.created2024-11-09
dc.description.abstractIn this thesis, we investigate the potential of machine learning (ML) techniques to enhance the accuracy and efficiency of cervical cancer diagnostics. The work's goal was to develop a machine learning-based application that can classify cervical cell images with good accuracy, providing an accessible and automated alternative to manual inspection.We used publicly available dataset (SIPaKMeD) single cell images. We extract features from the single cell images using different techniques and then filter some feature using filter selection approach to enhance model accuracy. We use different machine learning techniques to evaluate which one work better with our data set and the SVM model gives us the highest accuracy and precision. The trained model then used to predict the image classes percentage, which show the accuracy and confidence of the model on the prediction.
dc.description.courseProgramtervező informatikus
dc.description.degreeBSc/BA
dc.format.extent42
dc.identifier.urihttps://hdl.handle.net/2437/387358
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectmachine learning
dc.subjectcell classification
dc.subjectfeature extraction
dc.subjectImage processing
dc.subject.dspaceInformatics
dc.subject.dspaceBiology
dc.titleCervical Cell Classification using Machine Learning
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