Particulate Matter Levels Classification Using Modified and Combined ResNet Models with Low Features Extraction

Abstract

Smog is a serious environmental problem. It is an atmospheric pollutant that, if inhaled frequently, can lead to lung diseases such as asthma and bronchitis. One of the most dangerous air pollutants is particulate matter with a diameter of fewer than 2.5 micrometers (PM2.5), which may be breathed into the body and cause major health issues by introducing dangerous compounds deep into the lungs and bloodstream. In this research, a new convolutional neural network is proposed, by upgrading and parallelly stacking the two pre-trained models ResNet18 and ResNet50 to form a new modified-combined convolutional model (C-DCNN). Besides, we stacked another two columns of layers to extract the low features of ResNet18 and ResNet50 separately, to create finally four stacked columns of layers. The new model classifies images into different classes based on their PM2.5 concentration levels. To assess the suggested approach, an image augmentation is applied, then divided the images randomly (80% for the training progress,20% of the used training data for validation, and 20% for testing). The experimental results demonstrate that the proposed method increased the accuracy of level estimation with an accuracy increment equal to (6.25% at LR=0.