Authors: Naman Garg, Abhishek Bhadra, Shubh Gupta, Sonali Dhangar, Prof. S. P. Jadhav
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Pneumonia is a life-threatening respiratory disease that causes inflammation and fluid accumulation in the lungs. Chest X-ray is one of the most commonly used diagnostic tools to detect pneumonia. In recent years, deep learning techniques have been widely used in the medical field for image analysis and diagnosis. In this study, we propose a deep learning-based method for pneumonia detection using chest X-ray images. This study proposes a Convolutional Neural Network (CNN) based approach for detecting pneumonia using chest X-ray images. The proposed model consists of multiple layers of convolution, pooling, and fully connected layers, designed to extract features from input images and classify them into pneumonia and non-pneumonia categories. The model is trained on a publicly available dataset containing chest X-ray images of patients with and without pneumonia. The performance of the proposed model is evaluated using various metrics, including accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC-ROC). The results indicate that the proposed model achieved high accuracy, sensitivity, and specificity in detecting pneumonia from chest X-ray images, demonstrating its potential for clinical use in aiding radiologists in diagnosing and monitoring patients with pneumonia, particularly in cases where the diagnosis is challenging due to factors such as image quality or complex cases. Moreover, the proposed approach can be integrated into existing radiology workflows to automate the detection and classification of chest X-ray images, leading to faster and more accurate diagnoses, thereby improving patient outcomes.
Pneumonia is a common respiratory infection that can lead to severe complications if not diagnosed and treated promptly. Chest X-ray imaging is often used as a diagnostic tool to detect pneumonia, but it can be challenging to interpret due to the subjective nature of the interpretation process.
Automated approaches for detecting pneumonia from chest X-ray images have the potential to improve diagnosis accuracy, reduce the interpretation time, and aid rsadiologists in the diagnosis of complex cases.
Convolutional Neural Networks (CNNs) have been shown to be highly effective in analyzing medical images and identifying abnormalities in them. In recent years, CNN-based approaches have been proposed for the detection of pneumonia from chest X-ray images. The CNN-based approach involves training a deep neural network to learn the underlying features that distinguish between pneumonia and non-pneumonia cases. This approach has the potential to improve the accuracy and consistency of pneumonia detection from chest X-ray images. This study proposes a CNN-based approach for pneumonia detection from chest X-ray images. The proposed model is trained on a publicly available dataset of chest X-ray images of patients with and without pneumonia. The performance of the proposed model is evaluated using various metrics, including accuracy, precision, recall, F1 score, and AUC-ROC. The study aims to demonstrate the potential of CNN-based approaches for pneumonia detection and their ability to improve the accuracy and consistency of chest X-ray interpretation. The proposed approach has the potential to aid radiologists in diagnosing and monitoring patients with pneumonia, leading to improved patient outcomes..
Pneumonia is a significant public health problem that affects people of all ages, particularly children under five years of age and older adults. According to the World Health Organization (WHO), pneumonia is one of the leading causes of death among children worldwide, accounting for over 800,000 deaths annually.
Prompt diagnosis and treatment are crucial for managing pneumonia and reducing its associated morbidity and mortality. Chest X-ray imaging is one of the commonly used diagnostic tools for detecting pneumonia. However, the interpretation of chest X-ray images is often subjective and can be affected by various factors, such as the expertise of the radiologist, image quality, and the complexity of the case. As a result, the diagnosis of pneumonia from chest X-ray images can be challenging and time-consuming, leading to delays in treatment and potentially worse outcomes.
Chest X-ray imaging is one of the commonly used diagnostic tools for detecting pneumonia. However, the interpretation of chest X-ray images is often subjective and can be affected by various factors, such as the expertise of the radiologist, image quality, and the complexity of the case.
As a result, the diagnosis of pneumonia from chest X-ray images can be challenging and time-consuming, leading to delays in treatment and potentially worse outcomes.
In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown remarkable performance in medical image analysis and classification. CNN-based approaches have also been proposed for detecting pneumonia from chest X-ray images, with promising results.
III. LITERATURE SURVEY
Report titled - Discerning COVID-19 from mycoplasma and viral pneumonia on CT images via deep learning
Pneumonia is the swelling of the lung tissue and affects one or both lungs. It occurs as a result of infection with organisms such as bacteria, viruses and fungi. Even though its severity is variable, its symptoms usually include cough, diffi- culty breathing, fever, and chest pain. COVID-19 is a contagious respiratory disease caused by the virus SARS-CoV-2. COVID19 has similar symptoms to viral pneu- monia and the patients of COVID-19 may also be subject to secondary bacterial in- fections. This paper uses several deep learning methods and computed tomography (CT) images to distinguish COVID-19 from other infections such as mycoplasma and bacterial pneumonia, as well as viral pneumonia. The results show that for all three cases, ResNet-50 is one of the best performing architectures.
Author - Sertan Serte
Report titled - Pneumonia Detection: An Efficient Approach Using Deep Learning.
Pneumonia is one of the largest infectious diseases that cause death in children and elderly people across the globe. Pneumonia impacts all the elderly and young people’s families and children everywhere but is most prevalent in Sub- Saharan Africa and South Asia. In December 2019 Wuhan, a city of China was affected by deadly, gruesome Pneumonia which was declared a pandemic by World Health Organisation. But the reason for the outbreak was not clear to everyone. Later, the doctors identified the disease as a new species of coronavirus, also cur- rently known as COVID-19. The main motivation behind this research was to iden- tify Pneumonia just by using the X-Ray images of the patients.
Author - Ayush Pant
Report titled - Deep learning for mycoplasma pneumonia discrimination from pneumonias like COVID-19
Mycoplasma pneumonia is an atypical bacterial pneumonia with dry cough as its most common symptom. It may occur as a coinfection along with the newly discovered COVID19. It is difficult i n m edical diagnosis t o distinguish mycoplasma pneumonia from COVID-19, a disease that may cause pneumonia it- self. It is also difficult d ue t o s imilar s ymptoms t o distinguish mycoplasma pneumonia from typical viral pneumonia. This paper aims to lessen these diagnosis difficulties b y u sing several deep learning methods on computed tomography (CT) images to classify them as having mycoplasma pneumonia, typical viral pneumonia or COVID-19. The analyses of this paper indicate ResNet-18 and MobileNet-v2 architectures perform well during the differentiation of these diseases
Author - Ali Serener
The above figure shows the system architecture of pneumonia disease detection using Machine Learning. The input and output of the project is simple.
User upload its xray image which we also call as radiograph and gets the prediction whether he/she has pneumonia or not. The prediction is done by the model which is trained on dataset. The dataset contains thousands of images of chest x-ray. The dataset is processed thoroughly.
Firstly, Image processing is done and then the features are extracted from the dataset and parameters are detected. Then with the help of algorithm (in this case, CNN) extracted features are matched with the features of trained image in dataset. And then finally the output is shown which predicts whether pneumonia disease is present or not.
Here is a general methodology that can be followed:
It's important to note that the methodology above is a general guideline, and the specific steps and techniques used may vary depending on the specific problem and dataset.
We express our heartfelt appreciation to Prof. S.D. Lokhande for his invaluable support and guidance throughout the project, the principal of our college, and Dr. S.R. Ganorkar, the Head of the Department of Information Technology, for granting us permission to undertake this project and providing us with unrestricted access to the laboratory facilities. We also wish to express our heartfelt appreciation to our project guide, Prof. S.P. Jadhav, for dedicating time and effort to support us throughout the project. Additionally, we extend our thanks to the IT Department staff for their assistance. Lastly, we would like to thank our fellow classmates for their unwavering support and encouragement throughout the project.
In conclusion, CNNs have proven to be effective in detecting pneumonia from chest X-ray images. By using a well-structured methodology, including data collection, preprocessing, model architecture selection, training, evaluation, and interpretation, we can develop accurate and reliable models for pneumonia detection. CNNs have the potential to assist medical professionals in diagnosing pneumonia quickly and accurately, which can ultimately lead to improved patient outcomes. However, it is essential to acknowledge that these models are not a replacement for medical professionals\' expertise and should be used as an aid to support their decision-making process. Additionally, further research and development are necessary to improve the accuracy and reliability of these models and to address any ethical concerns that may arise from their use.
 Ranjan, E. Paul, S. Kapoor, S. Kar, Aupendu, Sethuraman, R. Sheet and Debdoot, ”Jointly Learning Convolutional Representations to Compress Radi- ological Images and Classify Thoracic Diseases in the Compressed Domain,” in Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP), ACM, Hyderabad, 2018.  B. Antin, J. Kravitz and E. Martayan, ”Detecting Pneumonia in Chest X- Rays with Supervised Learning,” 2017.  O. Stephen, M. Sain, U. J. Maduh and D.-U. Jeong, ”An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare,” Journal of HealthCare Engineering, vol. 2019, 2019.  C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin and M. J. Cardoso, ”Gen- eralised Dice overlap as a deep learning loss function for highly unbalanced segmentations,” in Deep  Brox, O. Ronneberger, P. Fischer and Thomas, ”U-Net: Convolutional Net- works for Biomedical Image Segmentation,” in International Conference on Medical image computing and computer-assisted intervention, 2015.  M. Tan and Q. V. Le, ”EfficientNet: Rethinking Model Scaling for Convo- lutional Neural Networks,” ICML, 2019.
Copyright © 2023 Naman Garg, Abhishek Bhadra, Shubh Gupta, Sonali Dhangar, Prof. S. P. Jadhav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.