Combining convolutional neural networks and spatial-channel “squeeze and excitation” block for multiple-label image classification

Abstract

In emergency rooms and intensive care units, catheters and tubes are used to keep critically ill patients alive. Appropriate catheter or tube insertion is crucial to avoiding serious complications. Such issues can be rectified if they are identified early. Chest X-rays are commonly used to assess catheter placement. Convolutional neural networks (CNN) have recently been found to enhance multi-label classification tasks on chest X-rays images. Furthermore, attention modules have shown the effect of enhancing spatial encoding on network feature maps. This research analyzed the experiments of each CNN model with different attention blocks. Resnet200D with batch normalization and spatial-channel squeeze and excitation block (BN+scSE) is the best architecture for multiple-label image classification on a chest X-rays dataset from National Institutes of Health Clinical Center (NIH) with multiple catheters and tubes. Then came EfficientNetB5 with BN+scSE and Inception_v3 with spatial squeeze and channel excitation block, respectively.