Enhancing the Classification of Pap Smear Images using ENN – TLBO classification Method

Abstract

The most common cancer among the women younger than 35 in developing countries is cervical cancer. It is a human papilloma virus disease. It should be identified earlier by Pap smear test and treated earlier to avoid the consequences. Pap test a colonoscopy is widely used to check the vagina and the cervix. The Pap smear test is the most effective medical test, but it causes difficulty under the microscope at the point of analysis. Automatic cancer detection is designed to unravel the downside. This identification process involves some image processing methods, such as segmentation, and an improved SVM classification method. In this paper, an efficient Elman Neural Network (ENN) collaborating with Teaching Learning Based Optimization (TLBO) algorithm is proposed to classify cancer using Pap Smear Test images. At first, an input image of Pap smear is converted into grey level from the RGB. The grey level image is preprocessed to eliminate unwanted noise produced and smoothened with Kuan Filter (KF). Active Contour Method (ACM) has been used to segment the identified cells from the Pap smear image. Features such as GLCM, haralick, solidity, shape, and other mathematical features are extracted for improving the accuracy. Classification has done using ENN-TLBO. TLBO is utilized for getting optimal weights during the training phase. Performance evaluation has done through the experimental outcomes where, ENN-TLBO yields good accuracy of 86.6%, than the prevailing algorithms such as Support Vector Machine, Radial Basis Function Neural Network classifiers.