Diagnosis of Cervical Cancer Using Hybrid Multilayered Perceptron (HMLP) Network

摘要

Cancer of the cervix is the second most common cancer among females in Malaysia after breast cancer. In most cases, cervical cancer takes many years to develop from normal to advanced stage. Therefore, the mortality related to cervical cancer can be reduced through early detection and treatment. Pap test is one of the early diagnosis that should be done to reduce the mortality rate related to cervical cancer. Neverthenles, low accuracy, sensitivity and specificity become a problem in diagnosing cervical cancer by using the Pap test. Recently, artificial intelligent based on neural network such as radial basis function, multi-layered perceptron and modular knowledge-based network have been implemented widely as cervical cancer diagnosis system. The networks is used to classify the cervical cells into normal and abnormal cells. In this paper, a hybrid multi-layered perceptron using recursive least square algorithm is introduced to diagnose the cervical cancer. The network has high ability to classify the cervical cells into normal, low-grade squamous intraepithelial lesions and high-grade squamous intraepithelial lesions. Furthermore, it has been prove to achieve better accuracy, sensitivity and specificity with smaller false negative and false positive compared to the conventional techniques. The results also proved that by using the network which has superior ability to be implemented as cervical cancer diagnosis system, the Pap test performance can be improved.