# An Improved VGG16 Model for Pneumonia Image Classification
## 1 Introduction  
Most recent deep learning breakthroughs are related to convolutional neural networks (CNNs), which are also the main developing area for deep neural networks (DNNs). Modern CNN approaches can be more accurate than humans for image recognition. The ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [1] is a highly representative academic competition for machine vision solutions based on image data provided by ImageNet. The main dataset comprises more than 14 million marked images, with a smaller subset sampled for the yearly ILSVRC. The best ILSVRC result prior to 2012 achieved a 26% error rate. However, a CNN model based on AlexNet [2] reduced the error rate to 16.4% in 2012, winning the championship. Subsequent studies have used various CNN approaches, some going on to become major and well-known CNN architectures. Many current products use deep learning technologies, most of which relate to image recognition. Recent hardware breakthroughs and significant improvements have brought a strong focus onto deep learning, with CNN models becoming the most popular approaches for image recognition, image segmentation [3; 4; 5] and object recognition. Image recognition refers to the process where a machine is trained using CNNs to extract important featuresfrom large image datasets, combine them into a feature map and perform recognition by connecting neurons. The approach has been successfully applied to various areas, such as handwriting recognition [6; 7; 8], face recognition [9; 10; 11], automatic driving vehicles [12], video surveillance [13] and medical image recognition [14; 15; 16; 17]. With the development of computer technology applied to the medical field, whether through basic medical applications, disease treatments, clinical trials or new drug therapies, all such applications involve data acquisition, management and analysis. Therefore, determining how modern medical information can be used to provide the required data is an important key to modern medical research. Medical services mainly include telemedicine, information provided through internet applications and digitization of medical information. In this way, we more accurately and quickly confirm a patient's physical condition and determine how best to treat a patient, thereby improving the quality of medical care. Smart healthcare can help us to establish an effective clinical decision support system to improve work efficiency and the quality of diagnosis and treatment. This is of particular importance in the aging population in society, which has many medical problems. In addition, the COVID-19 [18; 19; 20; 21; 22] outbreak in 2020 greatly increased the demand for medical information processing. Therefore, medical information is combined with quantitative medical research; in addition, assistance in diagnosis from an objective perspective is a trend at present. The development of big data systems has also enabled the systematic acquisition and integration of medical images [23; 24; 25; 26; 27]. Furthermore, traditional image processing algorithms have gradually been replaced by deep learning algorithms. Recent deep learning and machine learning developments mean that traditional image processing method performance for image recognition is no longer comparable to that of neural network (NN)-based approaches. Consequently, many studies have proposed optimized deep learning algorithms to improve image recognition accuracy for various recognition scenarios. CNN is the most prominent approach for image recognition, improving recognition accuracy by increasing hidden layer depths and effectively acquiring more characteristic parameters. Successful image recognition applications include face, object, and license plate recognition, but medical image recognition is less common due to difficulties acquiring medical images and poor understanding regarding how diseases appear in the various images.