Detecting Cervix Type Using Deep learning and GPU

摘要

Cervical cancer is the second most occurring cancer in women of all age groups. It causes cells on the cervix to grow out of control. Cervical cancer is caused by a virus called human papillomavirus aka HPV. In the early stages of cancer, there will be very little symptoms which make it difficult to detect. If cancer is detected at an early stage, then proper and effective medication can be started at the right time. Usual methods available for detection of cervical cancer largely depend on human expertise. With the advancements in medical imaging technology, computerized methods were also developed to detect the cancerous cells at an early stage. The type of treatment for cervical cancer is primarily determined by the cervix type of the patient and hence its type detection is very important. Thus, we have proposed a method to classify the cervix type using deep learning technology. A CNN model is created and trained from the scratch, along with two other models which are trained using transfer learning technology. From the experimental results, a validation accuracy of 0.6523 is achieved. We also trained the parallel models using GPU and speed of about six fold (x6) is achieved.