Lung and colon classification using improved local Fisher discriminant analysis with ANFIS

Abstract

Cancer has a high mortality rate due to its aggressiveness, potential for metastasis, and heterogeneity. There are several types of cancers that are common throughout the world, including lung cancer and colon cancer. Radiologists have developed several expert systems to assist in the diagnosis of lung cancer over the years. However, this requires accurate research. Therefore, in this paper, an automatic lung and colon cancer classification model based on a machine learning algorithm is proposed. Initially, the histopathological images are collected from the dataset. Then, to reduce the noise present in the input image, we apply the adaptive median filter. After noise removal, we use novel feature extraction techniques, gray-level histogram (MGH) of moments, local binary pattern (LBP) features, and gray-level co-occurrence matrix (GLCM) features and morphological features to extract features. Since the large number of features is a major obstacle to the classification process, improved local Fisher discriminant analysis (ILFDA) is used to reduce the dimensionality after feature extraction. After feature selection, the selected features are given to an enhanced ANFIS classifier to classify an image as normal or abnormal. The performance of the proposed approach is analyzed based on different metrics. The proposed method is implemented in Python.