# Multiple Feature Integration for Classification of Thoracic Disease in Chest Radiography
## 1 Introduction
However, CNNs require a large number of training images to avoid overfitting issues, and it is still unclear how well deep learning-based techniques can scale up to thousands of patients because previous studies were evaluated on the small-to-middle scale of several hundred patients. Intuitively, the features are the predominant factor in image representation basis. An image can be seen as a bunch of local patches regardless of the spatial information, which can easily be obtained by clustering shallow handcrafted features such as scale invariant feature transform (SIFT) [23], local binary patterns (LBPs) [24], and histogram of oriented gradients (HOG) [25]. Extracting multiple shallow handcrafted features and integrating them is a promising way to further improve the performance, compared to extracting a single feature [26; 27; 28]. Regarding the task of bioimage classification, learned descriptors have been recently explored. For example, [PERSON] et al. [29] devised an automatic feature extraction method based on class-specific dictionaries to diagnose the level of ovarian carcinomas, and [PERSON] et al. [30] proposed the combination of handcrafted and learned descriptors using Riesz wavelets to differentiate the irregularity of brain cells. However, these features may rely heavily on the local feature descriptors, which limit the dataset generalization ability. [PERSON] et al. [31] proposed a method to fuse different deep features. Typically, features extracted by the top layers near to the classification layers strongly depend on the training set, whereas the features resembling Gabor filters and color blobs from the bottom layers tend to be transferable for many different classification problems [32]. These studies motivate the extended applications of CNNs or ensemble models on bioimage datasets [33; 34] as feature extractors. Learned features were then treated as SIFT and LBP features to be fed into other classifiers such as SVM. Besides, [PERSON] et al. [35] presented the first use of deep features extracted from VGGNet [8] at multiple scales and the combination of deep features with texture and color-based handcrafted features for coral classification improvement. In this work, it is of great interest and importance to exploit the integration of both handcrafted features and deep features for the 14 thoracic disease classification tasks. To sum up, the main contributions of this study are as follows. * We utilize the efficient pretrained DenseNet-121 to visualize the class activation map (CAM) of pathological abnormalities from the ChestX-ray14 dataset. * We extract different types of shallow and deep features and select the coded features to generalize the best feature combination by extensive experiments. * We compare the classification results from different classifiers trained in a supervised manner. The remainder of the work is organized as follows. Section 2 briefly presents the related works on CXR disease classification. In Section 3, we describe our proposed approaches for CAM visualization and multiple feature integration for classification tasks in details. Section 4 introduces the ChestX-ray14 dataset and summarizes our experimental results. The work concludes in Section 5 with a short discussion and future works.