Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques

Venkataramana, Lokeswari; Prasad, D. Venkata Vara; Saraswathi, S; Mithumary, C M; Karthikeyan, R; Monika, N

doi:10.1007/s11517-022-02632-x

Cited by 17 publications

(12 citation statements)

References 29 publications

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“…A CNN classification model for COVID-19 in pneumonia (including viral and bacterial infected CXR) was developed by Venkataramana et al [ 54 ]. In addition, by using their method, they were able to distinguish TB patients from CXRs that were contaminated with pneumonia.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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Chest disease refers to a wide range of conditions affecting the lungs, such as COVID-19, lung cancer (LC), consolidation lung (COL), and many more. When diagnosing chest disorders medical professionals may be thrown off by the overlapping symptoms (such as fever, cough, sore throat, etc.). Additionally, researchers and medical professionals make use of chest X-rays (CXR), cough sounds, and computed tomography (CT) scans to diagnose chest disorders. The present study aims to classify the nine different conditions of chest disorders, including COVID-19, LC, COL, atelectasis (ATE), tuberculosis (TB), pneumothorax (PNEUTH), edema (EDE), pneumonia (PNEU). Thus, we suggested four novel convolutional neural network (CNN) models that train distinct image-level representations for nine different chest disease classifications by extracting features from images. Furthermore, the proposed CNN employed several new approaches such as a max-pooling layer, batch normalization layers (BANL), dropout, rank-based average pooling (RBAP), and multiple-way data generation (MWDG). The scalogram method is utilized to transform the sounds of coughing into a visual representation. Before beginning to train the model that has been developed, the SMOTE approach is used to calibrate the CXR and CT scans as well as the cough sound images (CSI) of nine different chest disorders. The CXR, CT scan, and CSI used for training and evaluating the proposed model come from 24 publicly available benchmark chest illness datasets. The classification performance of the proposed model is compared with that of seven baseline models, namely Vgg-19, ResNet-101, ResNet-50, DenseNet-121, EfficientNetB0, DenseNet-201, and Inception-V3, in addition to state-of-the-art (SOTA) classifiers. The effectiveness of the proposed model is further demonstrated by the results of the ablation experiments. The proposed model was successful in achieving an accuracy of 99.01%, making it superior to both the baseline models and the SOTA classifiers. As a result, the proposed approach is capable of offering significant support to radiologists and other medical professionals.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Malik,

Anees

2024

PLoS ONE

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“…Venkataramana et al [29] proposed a multi-level classification system that contains two models. The first model is a binary classification model that classifies TB and pneumonia.…”

Section: Joint Diseases Detection Studiesmentioning

confidence: 99%

“…A summary of literature review is presented in Table 1 containing studies employed for multiple/joint disease detection from chest X-ray images. The highest average accuracy achieved in the validation phase for multiple disease is obtained by [29] as 96.6% with SMOTE and deep learning as a four-class problem. Nonetheless, as far as the binary classification problems are concerned, the highest accuracy achieved by [34] was 98.54%.…”

Section: Joint Diseases Detection Studiesmentioning

confidence: 99%

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

Ahmed,

Rahman,

AlGhamdi

et al. 2023

Diagnostics

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Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

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“…The fuzzy Colouring technique is used for pre-processing, then MobileNetV2 and SqueezeNet, are used for features extraction, and finally, Support Vector Machines (SVM) are used to classify images into three classes (normal, pneumonic, and COVID-19 positive) [1]. In COVID-Net, a deep 121-layer dense convolutional neural network [2] [5] and MobileNet architecture could automatically detect and extract the essential COVID-19, Pneumonia, and normal image features with 97.8% accuracy. Ozturk et al showed that DarkCovidNet (you only look once (YOLO), with seventeen convolutional layers), can be used to classify the Xray images in real-time into similar three classes (COVID-19, Pneumonia, and normal) with 87%, and two classes (COVID-19 and non-COVID-19) with 98.08% accuracy.…”

Section: Literature Surveymentioning

confidence: 99%

“…The severe symptoms are difficulty in breathing or shortness of breath [1], chest pain, loss of speech and mobility, etc. In extreme cases, COVID-19 mainly affects in lungs [2]. The damage to lungs, ARDS, Pneumonia, etc., are subsequent and life-threatening.…”

Section: Introductionmentioning

confidence: 99%

A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection

Gumma¹,

Thiruvengatanadhan²,

Lakshmi³

et al. 2022

RIA

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Thorax diseases are most diagnosed through medical images and are manual and timeconsuming. The recent COVID-19 pandemic has demonstrated that machine learning systems can be an excellent option for classifying these medical images. However, a confidence classification in this context is the need. During COVID-19, we first need to detect and isolate COVID-19 patients. When it comes to diagnosing and preventing thoracic disorders, nothing beats the convenience and low cost of a chest X-ray. According to expert opinion on screening chest X-rays, abnormalities were most commonly found in the lungs and hearts. However, in fact, acquiring region-level annotation is costly, and model training mostly depends on image-level class labels in a poorly supervised way, making computeraided chest X-ray filtering a formidable obstacle. Hence, in this work, we propose a binary, multi-class, and multi-level classification model based on transfer learning models ResNet-50, InceptionNet, and VGG-19. After that, a multi-class classifier is used to know which class it mostly be-longs to. Finally, the multi-level classifier is used to know how many diseases the patient suffers from. This research presents a Binary Multi Class and Multi Level Classification with Dual Priority Labelling (BMCMLC-DPL) model for COVID-19 and other thorax disease detection. Using state-of-the-art deep neural networks (ResNet-50), we have shown how accurate the classification of COVID-19, along with 14 other chest diseases, can be performed. Our classification technique thus achieved an average training accuracy of 98.6% and a test accuracy of 96.52% for the first level of binary classification. For the second level of 16 class classification, our technique achieved a maximum training accuracy of 91.22% and test accuracy of 86.634% by using ResNet-50. However, due to the lack of multi-level COVID-19 patient data, multi-level classification is performed only on 14 classes, showing the state-of-the-art accuracy of the system.

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Classification of COVID-19 from tuberculosis and pneumonia using deep learning techniques

Cited by 17 publications

References 29 publications

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection

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