Abstract
ABSTRACT Classification 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.
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