The Effect of Image Dimension and Exposure Fusion Framework Enhancement in Pneumonia Detection Using Residual Neural Network

This study tries to find the performance of ResNet 50, 101 and 152 version 1 architecture to classify normal and pneumonia using chest X-ray. For the second goal, the effect of image dimension is investigated. Furthermore, the different distribution of training, validation and testing images in the dataset is explored, i.e. dataset A and B. 5,838 chest X-ray images are used in dataset A. For dataset B, 5,856 images are used. These images are distributed randomly as training, validation and testing images with compositions of 80%, 10% and 10%. For each dataset, 12 image dimensions are used, i.e., 96 × 96; 128 × 128; 160 × 160; 192 × 192; 224 × 224; 256 × 256; 288 × 288; 320 × 320; 352 × 352; 384 × 384; 416 × 416 and 448 × 448 pixels. Moreover, this study used exposure fusion framework as image enhancement in the preprocessing stage. In general, ResNet 152 architecture has better performance than ResNet 50 and 152. However, the best performance is achieved by dataset A using ResNet 50 and an image dimension of 384 × 384 pixels. This model has an accuracy, sensitivity, specificity, precision, F1-score and ROC of 97.6%, 99.3%, 93.1%, 97.5%, 98.4% and 96.2%. The pneumonia detection using ResNet is affected by the distribution of training, validation and testing images. Furthermore, there is no correlation between the image dimension and the detection performance.