Detection of Cervical Cancer with Texture Analysis using Machine Learning Models
T. J. Nagalakshmi, N. Nalini, P. Jagadeesh, P. Shyamala Bharathi, V. Amudha, G. Ramkumar
Abstract
In women, cervical cancer is a commonly occurring more dangerous cancer. 90% of the women suffer from this cancer. The cancer begins in thin, flat, flat epithelial cells on the surface of the external neck of the cervix. With the early detection it is curable. But diagnosis is complicated. To identify the cancer nowadays machine learning algorithms, deep learning algorithms, fuzzy logics and artificial intelligence are used. In this proposed system, to identify the cancer cells, the gray scale images were used. The texture is analyzed with a Gabor filter and high impact features were identified with histogram equalization. SVM used for the classification of cancer and non-cancer cells. The obtained accuracy is 97%.