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GoogLeNet-Based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis

Transfer learning techniques are recently preferred for the computer aided diagnosis (CAD) of variety of diseases, as it makes the classification feasible from limited training dataset. In this work, an ensemble FCNet classifier is proposed to classify hepatic lesions from the deep features extracted using GoogleNet–LReLU transfer learning approachs. In the existing GoogLeNet architecture three modifications are done: ReLU activation functions in the inception modules are replaced by leaky ReLU activation function; a stack of three fully connected layers are included before the classification layer; and deep features of different level of abstraction extracted from the output of every inception layer given as classifier input in order to significantly enhance the classifier performance. The performance of the proposed classifier by the virtue of the above mentioned modifications is tested on six classes of liver CT images namely normal, hepatocellular carcinoma, hemangioma, cyst, abscess and liver metastasis. The results presented in this work demonstrate the efficacy of the proposed classifier design in achieving better classification accuracy.