A survey of different methods for automated diagnosis of cervical cancer in pap-smear image

Abstract

A cancer type’s early diagnosis as well as classification can facilitate the patient’s subsequent clinical management. Cervical cancer (CC) grades as the 4^th most existent cancer universally affecting women and also its timely discovery offers the chance to protect lives. The CC’s automated diagnosis and also its classification as of the Pap-Smear (PS) images has developed as a requirement since it facilitates reliable, accurate and also well-timed examination of the condition’s growth. Diverse algorithms and methodologies are utilized aimed at CC’s automated screening via segmenting and categorizing the CC cells into diverse categories. This work explicates the survey on the CC’s diagnosis in the PS image. This study highlights the latest studies regarding cervical cancer diagnosis, like PS image enhancement (IE), the PS image’s automated segmentation, cervical cells’ features in PS image examination, and automated PS analysis. Lastly, the diverse diagnosis technique’s performances are analogized centred on the accuracy metric. For both the single-cell and multi-cell images, the comparison examination is executed.