Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Sharma, Chandra Mani; Goyal, Lakshay; Chariar, Vijayaraghavan M.; Sharma, Navel

doi:10.1155/2022/9036457

Cited by 29 publications

(7 citation statements)

References 40 publications

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“…Healthcare informatics can benefit significantly from machine learning. It can be used for the prognosis, classification, and diagnosis of diseases [6][7][8][9][10]. One of the most popular radiological tests for lung disease screening is the chest X-ray.…”

Section: Motivationmentioning

confidence: 99%

A Novel Dual Feature Extraction using Fine-Tuned ResNet with GWO and Deep Dense Neural Network for Multiple Lung Disease Classification

Kauhsik,

khan,

Rahmani

et al. 2024

Preprint

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Lung diseases are one of the primary causes of mortality worldwide. The majority of lung disorders are not discovered until they have progressed significantly. Computer Aided Diagnosis (CADx) systems allow for immediate and earlier diagnosis and are being expanded. This study investigates the feasibility of employing methods for learning features from fine-tuned adaptive learning rate Deep Learning (DL) architectures to provide robust and comprehensive features on the NIH Chest X-ray Dataset for three classes (Cardiomegaly, Emphysema, and Hernia) lung disease. A novel dual feature extraction using residual networks with a nature-inspired Gray Wolf Optimization (GWO) algorithm and Deep Dense Neural Network (ResNet-GWO-DD) is proposed in this study. Dual feature extraction is experimented with using two fine-tuned ResNet-50 and ResNet-101 Transfer Learning (TL) architectures. The global best optimal extracted features were optimized using GWO and are further combined for classification using a Deep Dense Neural Network. The dual learning of deep features using ResNet-50 and ResNet-101 helps the GWO to learn the global best optimal features. These dual learning capabilities greatly enhance the performance of the proposed model and achieve significant accuracy while comparing the state-of-the-art methods. The performance of the proposed method is further evaluated using three different optimizers such as Adam, Stochastic Gradient Descent (SGD), and Continuous Coin Betting (COCOB). Deep features extracted using GWO and optimizer Adam have yielded maximum accuracy of 99.68%, 96.63%, and 96.58% for Hernia, Emphysema, and Cardiomegaly respectively compared to SGD and COCOB.

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Section: Motivationmentioning

confidence: 99%

A Novel Dual Feature Extraction using Fine-Tuned ResNet with GWO and Deep Dense Neural Network for Multiple Lung Disease Classification

Kauhsik,

khan,

Rahmani

et al. 2024

Preprint

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“…To enhance evaluation accuracy, consider fine-tuning the Student model trained on pseudo labels using labeled X-ray images. The Teacher Network uses ResNet-50 [105,142] as its CNN model backbone, while InceptionResNet-V2 [143] serves as an alternative, known for its superior performance in supervised learning tasks. The parameters of the Student network is updated based on minimizing the cross-entropy (CE) loss.…”

Section: Self-trainingmentioning

confidence: 99%

A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image Classification

Shakya,

Alavi,

Porteous

et al. 2024

Information

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Deep semi-supervised learning (DSSL) is a machine learning paradigm that blends supervised and unsupervised learning techniques to improve the performance of various models in computer vision tasks. Medical image classification plays a crucial role in disease diagnosis, treatment planning, and patient care. However, obtaining labeled medical image data is often expensive and time-consuming for medical practitioners, leading to limited labeled datasets. DSSL techniques aim to address this challenge, particularly in various medical image tasks, to improve model generalization and performance. DSSL models leverage both the labeled information, which provides explicit supervision, and the unlabeled data, which can provide additional information about the underlying data distribution. That offers a practical solution to resource-intensive demands of data annotation, and enhances the model’s ability to generalize across diverse and previously unseen data landscapes. The present study provides a critical review of various DSSL approaches and their effectiveness and challenges in enhancing medical image classification tasks. The study categorized DSSL techniques into six classes: consistency regularization method, deep adversarial method, pseudo-learning method, graph-based method, multi-label method, and hybrid method. Further, a comparative analysis of performance for six considered methods is conducted using existing studies. The referenced studies have employed metrics such as accuracy, sensitivity, specificity, AUC-ROC, and F1 score to evaluate the performance of DSSL methods on different medical image datasets. Additionally, challenges of the datasets, such as heterogeneity, limited labeled data, and model interpretability, were discussed and highlighted in the context of DSSL for medical image classification. The current review provides future directions and considerations to researchers to further address the challenges and take full advantage of these methods in clinical practices.

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“…Using 750 chest X-ray images, the accuracy of detecting COVID-19 was found to be 90.7%, which needed to be improved, as did the number of data samples considered in this work. Sharma et al, [ 21 ] developed a model using hybrid Inception-ResNet-v2 to distinguish three classes of CXR images, such as COVID-19-positive patients, pneumonia-affected, and normal patients, with an accuracy of 98.66%. The accuracy of the proposed technique was also compared to other DL, ML and transfer learning methods, but it could not be implemented in a commercial setting.…”

Section: Related Workmentioning

confidence: 99%

RVCNet: A hybrid deep neural network framework for the diagnosis of lung diseases

Alam,

Podder,

Mondal

2023

PLoS ONE

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Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. Existing literature indicates that more research is needed to correctly classify lung diseases in the presence of multiple classes for different radiographic imaging datasets. As a result, this paper proposes RVCNet, a hybrid deep neural network framework for predicting lung diseases from an X-ray dataset of multiple classes. This framework is developed based on the ideas of three deep learning techniques: ResNet101V2, VGG19, and a basic CNN model. In the feature extraction phase of this new hybrid architecture, hyperparameter fine-tuning is used. Additional layers, such as batch normalization, dropout, and a few dense layers, are applied in the classification phase. The proposed method is applied to a dataset of COVID-19, non-COVID lung infections, viral pneumonia, and normal patients’ X-ray images. The experiments take into account 2262 training and 252 testing images. Results show that with the Nadam optimizer, the proposed algorithm has an overall classification accuracy, AUC, precision, recall, and F1-score of 91.27%, 92.31%, 90.48%, 98.30%, and 94.23%, respectively. Finally, these results are compared with some recent deep-learning models. For this four-class dataset, the proposed RVCNet has a classification accuracy of 91.27%, which is better than ResNet101V2, VGG19, VGG19 over CNN, and other stand-alone models. Finally, the application of the GRAD-CAM approach clearly interprets the classification of images by the RVCNet framework.

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Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing

Cited by 29 publications

References 40 publications

A Novel Dual Feature Extraction using Fine-Tuned ResNet with GWO and Deep Dense Neural Network for Multiple Lung Disease Classification

A Novel Dual Feature Extraction using Fine-Tuned ResNet with GWO and Deep Dense Neural Network for Multiple Lung Disease Classification

A Critical Analysis of Deep Semi-Supervised Learning Approaches for Enhanced Medical Image Classification

RVCNet: A hybrid deep neural network framework for the diagnosis of lung diseases

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