Hybrid feature extraction techniques for microscopic hepatic fibrosis classification. Issue 3 (10th January 2018) - Ashour, Dalia S.; Abou Rayia, Dina M.; Maher Ata, Mohamed; Ashour, Amira S.; Abd Elnaby, Mustafa M.
Abstract: Chronic liver diseases' hallmark is the fibrosis that results in liver function failure in advanced stages. One of the serious parasitic diseases affecting the liver tissues is schistosomiasis. Immunologic reactions to Schistosoma eggs leads to accumulation of collagen in the hepatic parenchyma causing fibrosis. Thus, monitoring and reporting the staging of the histopathological information related to liver fibrosis are essential for accurate diagnosis and therapy of the chronic liver diseases. Automated assessment of the microscopic liver tissue images is an essential process. For accurate and timeless assessment, an automated image analysis and classification of different stages of fibrosis can be employed as an efficient procedure. In this work, granuloma stages, namely cellular, fibrocellular, and fibrotic granulomas along with normal liver samples were classified after features extraction. In this work, a new hybrid combination of statistical features with empirical mode decomposition (EMD) is proposed. These combined features are further classified using the back‐propagation neural network (BPNN). A comparative study of the used classifier with the support vector machine is also conducted. The comparative results established that the BPNN achieved superior accuracy of 98.3% compared to the linear SVM, quadratic SVM, and cubic SVM that provided 85%, 84%, and 80%; respectively. In conclusion, this work is of special value that provides promising results for early prediction of the liver fibrosis in schistosomiais and other fibrotic liver diseases in no time with expected better prognosis after treatment. Abstract : Staging of liver fibrosis is essential for accurate diagnosis and therapy of liver diseases. A new hybrid combination of statistical features with empirical mode decomposition is proposed. BPNN achieved superior accuracy compared to linear SVM, quadratic SVM, and cubic SVM