Transductive cost-sensitive lung cancer image classification

Previous computer-aided lung cancer image classification methods are all cost-blind, which assume that the misdiagnosis (categorizing a cancerous image as a normal one or categorizing a normal image as a cancerous one) costs are equal. In addition, previous methods usually require experienced pathologists to label a large amount of images as training samples. To this end, a novel transductive cost-sensitive method is proposed for lung cancer image classification on needle biopsies specimens, which only requires the pathologist to label a small amount of images. The proposed method analyzes lung cancer images in the following procedures: (i) an image capturing procedure to capture images from the needle biopsies specimens; (ii) a preprocessing procedure to segment the individual cells from the captured images; (iii) a feature extraction procedure to extract features (i.e. shape, color, texture and statistical information) from the obtained individual cells; (iv) a codebook learning procedure to learn a codebook on the extracted features by adopting k-means clustering, which aims to represent each image as a histogram over different codewords; (v) an image classification procedure to predict labels for testing images using the proposed multi-class cost-sensitive Laplacian regularized least squares (mCLRLS). We evaluate the proposed method on a real-image set provided by Bayi Hospital, which contains 271 images including normal ones and four types of cancerous ones (squamous carcinoma, adenocarcinoma, small cell cancer and nuclear atypia). The experimental results demonstrate that the proposed method achieves a lower cancer-misdiagnosis rate and lower total misdiagnosis costs comparing with previous methods, which includes the supervised learning approach (kNN, mcSVM and MCMI-AdaBoost), semi-supervised learning approach (LapRLS) and cost-sensitive approach (CS-SVM). Meanwhile, the experiments also disclose that both transductive and cost-sensitive settings are useful when only a small amount of training images are available.