Since the advent of the Covid 19 infectious disease, a variety of studies have been conducted around the world in order to accurately forecast its outcome. Covid 19 is linked to the previous lung disease pneumonia, since many individuals died from severe chest congestion (pneumonic condition). The objective of this study is to use computerized x ray images to identify bacterial pneumonia, covid 19 viral pneumonia, other viral pneumonia, lung opacity and normal instinctively. It continues with a lengthy report on developments made in the accurate identification of pneumonia, followed by the authors' technique. For transfer learning, thirteen different deep Convolutional Neural Networks (CNNs) were used: AlexNet, ResNet18, ResNet50, ResNet101, VGG16, VGG19, MobileNetV2, GoogLeNet, DenseNet201, NasNetMobile, NasNetLarge, DarkNet and Inception-ResNet V2. Our data set consisting of 6530 images which are divided into five categories such as bacterial pneumonia, covid 19 viral pneumonia, other viral pneumonia, lung opacity and normal or healthy person’s x ray images, then the pre-processed images were trained for the transfer learning based classification task. Contrasting to other deep learning classification techniques with a large image data base, obtaining a large amount of pneumonia dataset for this classification task is difficult. As a result, we used several data augmentation techniques to enhance the CNN model's validation and classification accuracy as 99.50% and 98.20% respectively, and we produced significant validation accuracy. This DarkCVNet (DarkCovidNetwork) approach could be able to help with the trustworthiness and interpretability issues that come up frequently when dealing with medical images.