Discussion

In this study, the fine-tuning based on the concept of the transfer learning was performed to overcome the problem of insufficient training data. The Table 4-1 showed that the fine-tuned CaffeNet initialized weights by the model trained with nature images successfully boosts the performance on classifying EBUS images which is not similar to nature images. Directly training with limited scratch was not sufficient to optimize the parameters of the CaffeNet; hence the performance was not good. As with the previous study [38], it was helpful to perform the transfer leaning from the large scale annotated nature image datasets (ImageNet). To achieve better performance, the fusion of the fine-tuned CaffeNet and SVM was performed in this study. In Table 4-2, the fusion of the fine-tuned CaffeNet and SVM improved the specificity and the performance. It represented that the features extracting from the fine-tuned CaffeNet was discriminative and the classification ability of SVM outperformed the direct classification with the CaffeNet using the softmax layer. The reason might be that the generalization ability of the SVM was better than that of the softmax layer [39].

Moreover, according to the experimental results shown in Table 4-3, the performance of the handcrafted method (GLCM+SVM) was higher than that of the CaffeNet directly trained with scratch but lower than the fine-tuned CaffeNet and the fusion of the

limited training data was not plenty to optimize the parameters of the CaffeNet; hence, the automatic feature extractor of the CaffeNet could not produce powerful features than the handcrafted method. Besides the CaffeNet, there have recently been many deeper networks proposed with better performance in the ImageNet Large Scale Visual Recognition Competition [40], like the VGGNet, the GoogleNet and the ResNet. In our experiments, the performance and the training time of the fusion of the fine-tuned CaffeNet and SVM which only contains 8 layers was better than the fusion with other fine-tuned deeper CNNs. The reason might be that the deeper neural networks with more parameters, hence they need more training data and training time to optimize.

Although the proposed system achieved higher performance, there were two limitations. First, the quantity of the original dataset was not sufficient for fine-tuning the model to achieve the performance as the expert diagnosis. Although the data augmentation was performed to expand the dataset, the distribution of the dataset was not enlarged too much. To overcome the limitation, it was necessary to acquire more labeled data for fine-tuning. Besides, the images of the dataset came from only the same type of machine. Therefore, it was unconfirmed whether the proposed system was robust to the images from different types of machines. There was a need to acquire the images from different types of machines for fine-tuning the model to confirm the

Chapter 5

Conclusion and Future Work

In this study, a CAD system classifying lung lesions into benign or malignant was proposed. The system utilized data augmentation to expand the size of training data.

Then feature extraction based on fine-tuned CNN was performed. It was achieved by initializing the CaffeNet with the weight pre-trained on ImageNet and then the layers were fine-tuned with scratch. Moreover, the features were extracted from the fully connected layer 7 of CaffeNet. Furthermore, the SVM model was applied with the features to differentiate between benign and malignant lesions. According to the experiment results, the accuracy, sensitivity, specificity, PPV, NPV and the AUC of this system achieved 85.4% (140/164), 87.0% (94/108), 82.1% (46/56), 90.4% (94/104), 76.6% (46/60) and 0.8705, respectively. The results showed that the fusion of the fine-tuned CaffeNet and SVM system had potential to assist detecting lung cancer. In addition, the proposed method outperformed than the conventional handcrafted method and was the first to utilize deep learning for diagnosing EBUS images automatically. It decreased the manual operation and the time for diagnosis. In the future, it was required to expand the data set with the same quantity of benign and malignant lesions to enhance the optimization of the model. In addition, there was a need to evaluate the

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