DenseNet-CNN Architectural Model for Detection of Abnormality in Acute Pulmonary Edema

Abstract

Acute pulmonary edema (EPA) is a condition of emergency respiratory distress that results from the sudden and rapid build-up of fluid into the lungs. Rapid screening of EPA patients is necessary so that radiologists can make the prognosis as early as possible. In addition, reliance on the expert's knowledge of reasoning also hinders the diagnostic process. This research was conducted by developing an architectural model for machine learning systems with a deep learning approach. With the concept of representative learning, the denseNet-CNN algorithm connects each layer to another by means of a feed-forward. The data used is Image CXR-14 specifically labeled pulmonary edema pathology. The size of each CXR-14 image is 1024 × 1024 with a value of 8 bits grayscale. The size of each CXR-14 image is 1024 × 1024 with a value of 8 bits grayscale. The architectural model development stages consist of the preparation stage, data resampling, data training and data testing. Optimizer parameters used are Adam's optimizer, learning rate of 0.0001 and weight decay = 1e-5 and the loss used is binary cross entropy. The resulting mean AUROC analysis showed the sensitivity value of the 10% dataset was 71.493% and the specificity value of 10.011% was obtained at the second hold of the k-fold cross validation method after holdout validation, so that the resulting model was valid. The detection system developed from the denseNet-CNN model is expected to help radiologists identify abnormalities in CXR images quickly, precisely, and consistently. The denseNet-CNN model is also developed in the form of a heatmap visualization by localizing the features you are looking for. With localization in the form of a heat map, detection of pathological abnormalities of PEA is easier to do and to be recognized.