Cervical Cancer Diagnosis Using ERNN and PSO-GA
Rocky Yefrenes Dillak, Petrisia Widyasari Sudarmadji · 2021
Medical screening of cervical cancer is usually conducted by a pap smear test. However, this method still has many weaknesses, such as time-consuming and subjectivity in interpreting an image. This paper aims to develop a method used as a cervical cancer classification using pap-smear images. Five classes were used, namely: normal, CIN1, CIN2, CIN3, and malignant. The method work as follows: (i) pre-processes image using amoeba median filter and Gaussian filter (ii) nuclei detection, and segmentation (iii) extracts characteristics image using texture and shape analysis (iv) classify the image using Elman Recurrent Neural Network and Particle Swarm-Genetic Algorithm. Based on experiments conducted, the proposed method could detect and classify pap smear images with sensitivity, specificity, and accuracy of 93%, 98%, and 96%, respectively.