Authors: Shajy Lekshmanan; Varghese Paul; P. Smitha; K. Sujathan
Addresses: Department of Computer Science and Engineering, College of Engineering Karunagappally, Kollam, Kerala, India ' TocH Institute of Science and Technology, Eranakulam, Kerala, India ' Department of Computer Science and Engineering, College of Engineering Karunagappally, Kollam, Kerala, India ' Division of Cancer research, Regional Cancer Centre, Thiruvananthapuram, Kerala, India
Abstract: Early detection of lung cancer is very essential for proper treatment and to reduce the mortality rate. Through this paper we proposed a methodology for the early detection of lung cancer, using lung cytology image analysis. Before the segmentation, the images are pre-processed using Contrast Limited Adaptive Histogram Equalisation (CLAHE). The features are extracted from segmented PAP stained sputum cytology images, using Discrete Wavelet Transforms (DWT). Proper feature extraction gave a good classification result, which is to be obtained through a good feature extraction and classification method. Here, we used DWT to extract the features from sputum cytology images. The enhanced images are segmented using Otsu segmentation method. The segmented images are classified using Feed Forward Back Propagation Neural Network (FFBNN). The experimental results show that FFBNN gave better classification result using features from DWT-based matrix and obtained an accuracy level 92.9%.
Keywords: CLAHE; Otsu; DWT; discrete wavelet transforms; GLCM; sputum cytology; feature extraction; FFBNNs; classification; lung columnar cells; neural networks; early detection; lung cancer; lung cytology image analysis; medical images; sputum cytology images; image segmentation.
International Journal of Biomedical Engineering and Technology, 2016 Vol.20 No.4, pp.344 - 355
Received: 07 Jul 2015
Accepted: 23 Sep 2015
Published online: 17 May 2016 *