Detection and removal of artifacts in cervical Cytology images using Support Vector Machine

Cervical cancer is a leading cause of cancer-related deaths in women worldwide which kills more than 288,000 of them each year. 80% of these deaths occur in the developing countries like India, where there are no well established screening programmes. For implementing an effective and optimal mass screening the quantity of false positive rates should be controlled. The artifacts present in a massive order which are similar in size and shape to abnormal cells would cause the misclassification of cytology images in the screening process. This increases the false positive rate which can hinder the mass screening. Therefore the elimination of these artifacts plays a key role in designing a proper Classification strategy for the malignancy detection from Cytology images. This paper, deals with a new Pattern Recognition strategy on detection and removal of artifacts in cervical cytology images using Support Vector Machine (SVM).