Deep feature fusion and optimized feature selection based ensemble classification of liver lesions

ABSTRACTABSTRACTClassification of liver abnormalities is crucial for the early identification of liver cancer. In clinical settings, radiological professionals typically make diagnoses manually which is subjective, time-consuming and vulnerable to error. Therefore, there is still a demand for precise classification of liver diseases. We propose an Ensemble learning-based classification model to classify liver lesions on CT images. To excerpt all the essential facts from the image, deep feature fusion is incorporated by concatenating the features from pre-trained deep CNN models densenet201 and InceptionResnetV2. To minimize the feature space and boost classification accuracy, hybrid optimization methodologies, Genetic Algorithms and Ant Colony Optimization are applied. Finally, a Heterogeneous Ensemble classifier divides the retrieved features into four groups (liver abscess, liver cirrhosis, hepatocellular carcinoma, and metastasis). It is clearly seen and observed that 98.3% accuracy is contributed by ensemble classifier with the support of concatenated deep features and this classifier excels in all other ways and means.KEYWORDS: Liver Lesion ClassificationEnsemble LearningFeature FusionAnt Colony OptimizationGenetic AlgorithmsDeep Learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsA. AnishaA. Anisha received her B.E Degree in Computer Science and Engineering from Government College of Engineering, Tirunelveli and M.E degree in Computer Science and Engineering from V.L.B. Janakiammal College of Engineering and Technology, Coimbatore. She has been working as an Assistant Professor in St. Xavier's Catholic College of Engineering, Nagercoil. Her area of interest is Image Processing.G. JijiDr. G. Jiji received her B.E. degree in Electronics and Communication Engineering from St. Xavier's Catholic College of Engineering, Nagercoil and M.Tech degree in Power Electronics and Drives from Sastra University, Thanjavur, India. She had completed her Ph.D. degree from Anna University Chennai. She has been working as a Professor in Lord Jegannath College of Engineering and Technology, Nagercoil, India. She has published several research articles in reputed high-impact journals. Her research interest includes Power Converters, Image Processing and Smart Energy Materials.T. Ajith Bosco RajDr. T. Ajith Bosco Raj received his B.E. and M.E. degrees from St. Xavier's Catholic College of Engineering, Nagercoil, India. He had completed his Ph.D. degree in the College of Engineering, Guindy, Anna University Chennai. He has been working as a Professor in PSN College of Engineering and Technology, Tirunelveli, India. He has published several research articles in reputed high-impact journals. His research interest includes Power Converters, Image Processing, DSSC and Smart Energy Materials.