Cervical cancer cell images play an important part in diagnosing the cancer among the females worldwide. Existing noises, overlapping cells, mucus, blood and air artifacts in cervical cancer cell images makes their classification a hard task. It makes it difficult for both pathologists and intelligent systems to segment and classify them into normal, pre-cancerous and cancerous cells. However, true cell segmentation is needed for pathologists to make for accurate diagnosis. In this paper, a review of algorithms used for cervical cancer cell image classification is presented. This includes pre-processing steps (noise reduction and cell segmentation/without segmentation), feature extraction, and intelligent diagnosis systems and their evaluations. Finally, future research trends on cervical cell classification to achieve complete accuracy are described.
Cell Image Classification: A Comparative Overview
