An Efficient Cervical Image Segmentation Method Using Lagrange Dual Dictionary Learning in Convolutional Neural Network

Abstract

Cervical cancer lies in the fourth place of the most dominant disease found in women whereas; when detected on time and accurately saves life. A cancer diagnosis is complicated as the entire process has to be analyzed carefully in a random domain which can be overcome by Computer Assisted Diagnosis (CAD).  The significantprocess of a computer-assisted diagnostic system aiming at earlier detection of cervical cancer is segmenting the cancer cell precisely. This process offers several benefits such as improvement in diagnostic accuracy, reduction in time for diagnosis, and also improves the uniformity of the diagnostic results taken from different laboratories. In this paper, preprocessing and segmentation steps are discussed by proposing a novel supervised dictionary learning method for segmentation process. The algorithm is named as, Lagrange Dual Dictionary Learning Algorithm (LDDLA) in convolutional a neural network which aims to minimize the segmentation problems in a way by building specific dictionaries for everytype, and hence the input image can be segmented with the help of the dictionary related to the sparsest representation. As a result, the proposed method achieves 98.78% of accuracy, 94.7% recall, 79.3% of precision, 94.65% of training accuracy in 1.8 sec.