Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images

• URL: http://arxiv.org/abs/2310.06080v1
• Date: Mon, 9 Oct 2023 18:38:49 GMT
• Title: Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images
• Authors: Aditya Kulkarni, Guruprasad Parasnis, Harish Balasubramanian, Vansh Jain, Anmol Chokshi, Reena Sonkusare
• Abstract summary: 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to be serious global health concerns that affect millions of people worldwide. In medical practice, chest X-ray examinations have emerged as the norm for diagnosing diseases, particularly chest infections such as COVID-19. Paramedics and scientists are working intensively to create a reliable and precise approach for early-stage COVID-19 diagnosis in order to save lives. But with a variety of symptoms, medical diagnosis of these disorders poses special difficulties. It is essential to address their identification and timely diagnosis in order to successfully treat and prevent these illnesses. In this research, a multiclass classification approach using state-of-the-art methods for deep learning and image processing is proposed. This method takes into account the robustness and efficiency of the system in order to increase diagnostic precision of chest diseases. A comparison between a brand-new convolution neural network (CNN) and several transfer learning pre-trained models including VGG19, ResNet, DenseNet, EfficientNet, and InceptionNet is recommended. Publicly available and widely used research datasets like Shenzen, Montogomery, the multiclass Kaggle dataset and the NIH dataset were used to rigorously test the model. Recall, precision, F1-score, and Area Under Curve (AUC) score are used to evaluate and compare the performance of the proposed model. An AUC value of 0.95 for COVID-19, 0.99 for TB, and 0.98 for pneumonia is obtained using the proposed network. Recall and precision ratings of 0.95, 0.98, and 0.97, respectively, likewise met high standards.

Related papers

• Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images [3.829821362301428]
  This paper evaluates the applicability of the recent top ten state-of-the-art Deep Convolutional Neural Networks (CNNs) for automatically detecting COVID-19 infection using chest X-ray images. Our trained models MobileNet, EfficentNet, and InceptionV3 achieved a classification average accuracy of 95%, 95%, and 94%, respectively.
  arXiv  Detail & Related papers  (2022-07-01T02:23:23Z)
• A Novel Implementation of Machine Learning for the Efficient, Explainable Diagnosis of COVID-19 from Chest CT [0.0]
  The aim of this study was to take a novel approach in the machine learning-based detection of COVID-19 from chest CT scans. The proposed model attained an overall accuracy of 0.927 and a sensitivity of 0.958.
  arXiv  Detail & Related papers  (2022-06-15T18:35:22Z)
• The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays [64.02097860085202]
  Deep learning models have been developed to identify COVID-19 from chest X-rays. Results have been exceptional when training and testing on open-source data. Data analysis and model evaluations show that the popular open-source dataset COVIDx is not representative of the real clinical problem.
  arXiv  Detail & Related papers  (2021-09-14T10:59:11Z)
• Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 [6.941255691176647]
  We propose a novel residual network to automatically identify COVID-19 from other common pneumonia and normal people using CT images. Our method can differentiate COVID-19 from the other two classes with 94.7% accuracy, 93.73% sensitivity, 98.28% specificity, 95.26% F1-score, and an area under the receiver operating characteristic curve (AUC) of 0.99.
  arXiv  Detail & Related papers  (2021-05-14T11:59:47Z)
• Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification [83.6017225363714]
  deep learning has become the most powerful computer-aided diagnosis technology for improving disease identification performance. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods to improve a single model's disease identification performance.
  arXiv  Detail & Related papers  (2021-02-26T02:29:30Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis [7.471424290647929]
  We provide the largest publicly available lung ultrasound (US) dataset for COVID-19 consisting of 106 videos. We propose a frame-based convolutional neural network that correctly classifies COVID-19 US videos with a sensitivity of 0.98+-0.04 and a specificity of 0.91+-08.
  arXiv  Detail & Related papers  (2020-09-13T23:52:03Z)
• Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning [57.00601760750389]
  We present a severity score prediction model for COVID-19 pneumonia for frontal chest X-ray images. Such a tool can gauge severity of COVID-19 lung infections that can be used for escalation or de-escalation of care.
  arXiv  Detail & Related papers  (2020-05-24T23:13:16Z)
• Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning [48.05232274463484]
  Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
  arXiv  Detail & Related papers  (2020-05-06T15:19:15Z)
• COVID-DA: Deep Domain Adaptation from Typical Pneumonia to COVID-19 [92.4955073477381]
  The outbreak of novel coronavirus disease 2019 (COVID-19) has already infected millions of people and is still rapidly spreading all over the globe. Deep learning has been used recently as effective computer-aided means to improve diagnostic efficiency. We propose a new deep domain adaptation method for COVID-19 diagnosis, namely COVID-DA.
  arXiv  Detail & Related papers  (2020-04-30T03:13:40Z)
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images [0.0]
  CoroNet is a Deep Conceptional Neural Network model to automatically detect COVID-19 infection from chest X-ray images. The proposed model achieved an overall accuracy of 89.6% and the precision and recall rate for COVID-19 cases are 93% and 98.2%.
  arXiv  Detail & Related papers  (2020-04-10T07:46:07Z)

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.