Cervical cancer is a prevalent and fatal disease that affects women all over the world. This affects roughly 0.5 million women annually and kills over 0.3 million people. Recently, a significant amount of literature has emerged around the advancement of technologies for identifying cervical cancer cells in women. Previously, diagnosing cervical cancer was done manually, which could lead to false positives or negatives. The best way of interpreting Pap smear images and automatically diagnose cervical cancer are still up for debate among the researchers. Thus, as to encourage talented researchers in this field, an excellent, easily access and expert's validated data for cervical cell has been developed by previous researchers. In this study, datasets have been reviewed from previous studies that can be access for research and study purposes.
