Write a research paper about Pneumonia detection using deep learning
Sample Solution
This paper discusses the application of deep learning techniques to the task of detecting pneumonia in images. In particular, it outlines the state-of-the-art methods for training convolutional neural networks (CNNs) to detect and classify lung abnormalities such as those indicating pneumonia. We consider existing works on this topic, including architectures that have proven successful in other medical image classification tasks, how they were implemented for use in this specific task and discuss potential enhancements that can be made with further research and development. We also analyze open datasets suitable for training a model and propose possible metrics for evaluating its accuracy. Finally, we discuss some challenges associated with developing an accurate deep learning system for pneumonic disease detection.
Introduction
Pneumonia is a type of respiratory illness caused by bacteria or viruses invading one’s lungs. It is usually characterized by coughing, fever and difficulty breathing; however, diagnosing pneumonia from physical symptoms alone is difficult since many other diseases may present similar symptoms (CDC 2017). As such, imaging tests are often used to diagnose cases of suspected pneumonia reliably; these tests include x-rays or computed tomography (CT) scans which produce a clear visual representation of what’s going on inside the patient’s body (Kim et al., 2016). Unfortunately, manually interpreting these images takes considerable time and effort due to their complexity; this has led researchers to develop automated systems capable of identifying common features indicative of disease progression quickly and accurately (Lakhani et al., 2019). Currently much work is being done into designing artificial intelligence based systems specifically geared towards recognizing patterns in medical imagery that would otherwise require manual analysis. Deep learning algorithms show great promise when applied to medical imaging problems like classifying signs of pneumonia from CT scan data due to their ability to learn representations directly from raw data without requiring extensive feature engineering first (Krizhevsky et al., 2012).
Sample Solution
This paper discusses the application of deep learning techniques to the task of detecting pneumonia in images. In particular, it outlines the state-of-the-art methods for training convolutional neural networks (CNNs) to detect and classify lung abnormalities such as those indicating pneumonia. We consider existing works on this topic, including architectures that have proven successful in other medical image classification tasks, how they were implemented for use in this specific task and discuss potential enhancements that can be made with further research and development. We also analyze open datasets suitable for training a model and propose possible metrics for evaluating its accuracy. Finally, we discuss some challenges associated with developing an accurate deep learning system for pneumonic disease detection.
Introduction
Pneumonia is a type of respiratory illness caused by bacteria or viruses invading one’s lungs. It is usually characterized by coughing, fever and difficulty breathing; however, diagnosing pneumonia from physical symptoms alone is difficult since many other diseases may present similar symptoms (CDC 2017). As such, imaging tests are often used to diagnose cases of suspected pneumonia reliably; these tests include x-rays or computed tomography (CT) scans which produce a clear visual representation of what’s going on inside the patient’s body (Kim et al., 2016). Unfortunately, manually interpreting these images takes considerable time and effort due to their complexity; this has led researchers to develop automated systems capable of identifying common features indicative of disease progression quickly and accurately (Lakhani et al., 2019). Currently much work is being done into designing artificial intelligence based systems specifically geared towards recognizing patterns in medical imagery that would otherwise require manual analysis. Deep learning algorithms show great promise when applied to medical imaging problems like classifying signs of pneumonia from CT scan data due to their ability to learn representations directly from raw data without requiring extensive feature engineering first (Krizhevsky et al., 2012).
