A Deep Learning based Responsive Web Platform for Cervical Cancer Detection
Tejas Morkar, Suyash Sonawane, Aditya Mahajan, Swati V. Shinde · 2022 6th International Conference On Computing, Communication, Control And Automation (ICCUBEA · 2022
Classifying the cervical cells in the Pap smear images is a challenging problem. Many problems are associated with the complexity of the cell and contents that need to be identified. We attempt to make use of Deep Learning models to overcome the need of manual segmentation and feature extraction to finally predict if the given Pap Smear image shows signs of cancer or not. This system aims to aid the health care workers like doctors and pathologists in speeding up the process. In order to help maximize the comfort of the patient, we have developed a completely responsive web-based platform which is aided by our robust machine learning models to help the patient get their reports from the comfort of their homes and make it easier for early detection of cervical cancer.