Using Deep Learning to Improve Early Diagnosis of Pneumonia in Underdeveloped Countries

• URL: http://arxiv.org/abs/2210.05023v1
• Date: Mon, 10 Oct 2022 21:38:54 GMT
• Title: Using Deep Learning to Improve Early Diagnosis of Pneumonia in Underdeveloped Countries
• Authors: Kyler Larsen
• Abstract summary: The hypothesis is that a deep learning model can receive input in the form of an x-ray and produce a diagnosis with the equivalent accuracy of a physician. The model was trained on a set of 2000 x-ray images that have predetermined normal and abnormal lung findings. Results show that the algorithm tested was able to accurately identify abnormal lung findings an average of 82.5% of the time.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As advancements in technology and medicine are being made, many countries are still unable to access quality medical care due to cost and lack of qualified medical personnel. This discrepancy in healthcare has caused many preventable deaths, either due to lack of detection or lack of care. One of the most prevalent diseases in the world is pneumonia, an infection of the lungs that killed 2.56 million people worldwide in 2017. In this same year, the United States recorded a pneumonia death rate of 15.88 people per 100000 in population, while much of Sub-Saharan Africa, such as Chad and Guinea, experienced death rates of over 150 people per 100000. In sub-Saharan Africa, there is an extreme shortage of doctors and nurses, estimated to be around 2.4 million. The hypothesis being tested is that a deep learning model can receive input in the form of an x-ray and produce a diagnosis with the equivalent accuracy of a physician, compared to a prediagnosed image. The model used in this project is a modified convolutional neural network. The model was trained on a set of 2000 x-ray images that have predetermined normal and abnormal lung findings, and then tested on a set of 400 images that contains evenly split images of pneumonia and healthy lungs. For each computer-run test, data was collected on a base measurement of accuracy, as well as more specific metrics such as specificity and sensitivity. Results show that the algorithm tested was able to accurately identify abnormal lung findings an average of 82.5% of the time. The model achieved a maximum specificity of 98.5% and a maximum sensitivity of 90% separately, and the highest simultaneous values of these two metrics was a sensitivity of 90% and a specificity of 78.5%. This research can be further improved by testing other deep learning models as well as machine learning models to improve the metric scores and chance of correct diagnoses.

Related papers

• Empowering Tuberculosis Screening with Explainable Self-Supervised Deep Neural Networks [66.59360534642579]
  Tuberculosis persists as a global health crisis, especially in resource-limited populations and remote regions. In this work, we introduce an explainable self-supervised self-train learning network tailored for tuberculosis case screening. The network achieves an outstanding overall accuracy of 98.14% and demonstrates high recall and precision rates of 95.72% and 99.44%, respectively.
  arXiv  Detail & Related papers  (2024-06-19T18:10:06Z)
• Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
  Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals. Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
  arXiv  Detail & Related papers  (2024-04-26T16:39:50Z)
• Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images [0.0]
  Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to be serious global health concerns. Paramedics and scientists are working intensively to create a reliable and precise approach for early-stage COVID-19 diagnosis.
  arXiv  Detail & Related papers  (2023-10-09T18:38:49Z)
• Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populations [64.91773761529183]
  Primary open-angle glaucoma (POAG) is the leading cause of blindness in the United States. Deep learning has been widely used to detect POAG using fundus images. Human bias in clinical diagnosis may be reflected and amplified in the widely-used deep learning models.
  arXiv  Detail & Related papers  (2023-01-26T18:53:09Z)
• A Comparison Study of Deep CNN Architecture in Detecting of Pneumonia [0.0]
  Pneumonia, a respiratory infection brought on by bacteria or viruses, affects a large number of people. Deep convolutional neural network to classify plant diseases based on images and tested its performance. DenseNet201 achieves state-of-the-art performance with a significantly a smaller number of parameters and within a reasonable computing time.
  arXiv  Detail & Related papers  (2022-12-30T14:37:32Z)
• Predicting Pneumonia and Region Detection from X-Ray Images using Deep Neural Network [0.0]
  Pneumonia is an infection caused by both bacteria and viruses through the inflammation of a person's lung air sacs. In this paper, an algorithm was proposed that receives x-ray images as input and verifies whether this patient is infected by Pneumonia. The model has achieved an accuracy of 90.6 percent which confirms that the model is effective.
  arXiv  Detail & Related papers  (2021-01-19T16:43:05Z)
• Deep Learning Methods for Screening Pulmonary Tuberculosis Using Chest X-rays [0.974672460306765]
  The presented deep learning pipeline consists of three different state of the art deep learning architectures, to generate, segment and classify lung X-rays. We were able to achieve classification accuracy of 97.1% (Youden's index-0.941,sensitivity of 97.9% and specificity of 96.2%) which is a considerable improvement compared to the existing work in the literature.
  arXiv  Detail & Related papers  (2020-12-25T14:21:35Z)
• Timely Tracking of Infection Status of Individuals in a Population [70.21702849459986]
  We consider real-time timely tracking of infection status of individuals in a population. In this work, a health care provider wants to detect infected people as well as people who recovered from the disease.
  arXiv  Detail & Related papers  (2020-12-24T18:49:22Z)
• An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images [0.0]
  More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. It is not uncommon to overlook pneumonia detection for a well-trained radiologist, which triggers the need for improvement in the diagnosis's accuracy. We trained, fine-tuned the state-of-the-art deep learning models such as InceptionResNet, MobileNetV2, Xception, DenseNet201, and ResNet152V2 to classify pneumonia accurately.
  arXiv  Detail & Related papers  (2020-11-11T04:50:06Z)
• Deep Learning for Automatic Pneumonia Detection [72.55423549641714]
  Pneumonia is the leading cause of death among young children and one of the top mortality causes worldwide. Computer-aided diagnosis systems showed the potential for improving diagnostic accuracy. We develop the computational approach for pneumonia regions detection based on single-shot detectors, squeeze-and-excitation deep convolution neural networks, augmentations and multi-task learning.
  arXiv  Detail & Related papers  (2020-05-28T10:54:34Z)
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection [86.81773672627406]
  Clusters of viral pneumonia during a short period of time may be a harbinger of an outbreak or pandemic, like SARS, MERS, and recent COVID-19. Rapid and accurate detection of viral pneumonia using chest X-ray can be significantly useful in large-scale screening and epidemic prevention. Viral pneumonia often have diverse causes and exhibit notably different visual appearances on X-ray images.
  arXiv  Detail & Related papers  (2020-03-27T11:32:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.