Analysing X-Ray Images to Detect Lung Diseases Using DenseNet-169 technique

Nair, Kartik; Deshpande, Atharva; Guntuka, Ritesh; Patil, Anita

doi:10.2139/ssrn.4111864

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…Given the widespread prevalence of lung illnesses, particularly in resource-limited locations, such deep learning algorithms show potential for improving early detection and patient outcomes, adding to the larger global effort to battle lung diseases. Further research and clinical validation may pave the path for these models to be used in real-world healthcare settings [47].…”

Section: Literature Reviewmentioning

confidence: 99%

MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model

Ahoor,

Arif,

Sajid

et al. 2023

Diagnostics

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The lungs are critical components of the respiratory system because they allow for the exchange of oxygen and carbon dioxide within our bodies. However, a variety of conditions can affect the lungs, resulting in serious health consequences. Lung disease treatment aims to control its severity, which is usually irrevocable. The fundamental objective of this endeavor is to build a consistent and automated approach for establishing the intensity of lung illness. This paper describes MixNet-LD, a unique automated approach aimed at identifying and categorizing the severity of lung illnesses using an upgraded pre-trained MixNet model. One of the first steps in developing the MixNet-LD system was to build a pre-processing strategy that uses Grad-Cam to decrease noise, highlight irregularities, and eventually improve the classification performance of lung illnesses. Data augmentation strategies were used to rectify the dataset’s unbalanced distribution of classes and prevent overfitting. Furthermore, dense blocks were used to improve classification outcomes across the four severity categories of lung disorders. In practice, the MixNet-LD model achieves cutting-edge performance while maintaining model size and manageable complexity. The proposed approach was tested using a variety of datasets gathered from credible internet sources as well as a novel private dataset known as Pak-Lungs. A pre-trained model was used on the dataset to obtain important characteristics from lung disease images. The pictures were then categorized into categories such as normal, COVID-19, pneumonia, tuberculosis, and lung cancer using a linear layer of the SVM classifier with a linear activation function. The MixNet-LD system underwent testing in four distinct tests and achieved a remarkable accuracy of 98.5% on the difficult lung disease dataset. The acquired findings and comparisons demonstrate the MixNet-LD system’s improved performance and learning capabilities. These findings show that the proposed approach may effectively increase the accuracy of classification models in medicinal image investigations. This research helps to develop new strategies for effective medical image processing in clinical settings.

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Section: Literature Reviewmentioning

confidence: 99%

MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model

Ahoor,

Arif,

Sajid

et al. 2023

Diagnostics

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“…Currently, most approaches are aimed to detect COVID-19 and pneumonia, however, some works focus on the detection of other pulmonary diseases. For example, work [4] introduced the architecture, based on DenseNet-169, which is aimed to classify X-rays into COVID-19, Pneumonia, Tuberculosis and normal. The achieved accuracy is 91%.…”

Section: Related Work a Pulmonary Diseases Classification Using Deep ...mentioning

confidence: 99%

Chest X-ray Image Analysis using Convolutional Vision Transformer

Mezina¹,

Bürget²

2023

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected Papers.

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In recent years, computer techniques for clinical image analysis have been improved significantly, especially because of the pandemic situation. Most recent approaches are focused on the detection of viral pneumonia or COVID-19 diseases. However, there is less attention to common pulmonary diseases, such as fibrosis, infiltration and others. This paper introduces the neural network, which is aimed to detect 14 pulmonary diseases. This model is composed of two branches: global, which is the InceptionNetV3, and local, which consists of Inception modules and a modified Vision Transformer. Additionally, the Asymmetric Loss function was utilized to deal with the problem of multilabel classification. The proposed model has achieved an AUC of 0.8012 and an accuracy of 0.7429, which outperforms the well-known classification models.

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“…Each layer gathers data from the layers above it. Because each layer receives feature maps from all preceding layers, the network may be narrower and tighter, resulting in fewer channels (Nair, 2022). The DenseNet model, a pre-trained convolutional neural network, was employed in this study to harness the benefits of transfer learning.…”

Section: Densenet-169mentioning

confidence: 99%

Brain Tumor Detection Using Convolutional Neural Networks: A Comparative Study

Ojha,

Maharjan,

Acharya

2023

Int. J. Engin. Technol.

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Using Magnetic Resonance Imaging (MRI) images to detect brain tumors by medical practitioners is mundane and prone to errors. Misdiagnosis of brain tumors can be life-threatening, so to lessen misdiagnosis, computational techniques can be used in concert with medical professionals. Deep learning approaches have been gaining popularity in modeling and developing systems for medical image processing that can detect abnormalities quickly. The methods proposed herein are based on Convolutional Neural Networks (CNN) trained on the 'BR35H::Brain Tumor Detection 2020' dataset. A custom CNN architecture was designed, followed by the utilization of transfer learning with four pre-trained models: InceptionV3, ResNet101, VGG19, and DenseNet169 and a comparative analysis of these architectures has been presented in this paper. The experimental results show that the DenseNet169 model outperformed other models with a training accuracy of 99.83 %, test accuracy of 99.66%, precision of 99.67%, and recall of 99.67%. Additionally, ResNet101 has a 95.92% test accuracy, VGG19 has a test accuracy of 97.83%, the custom architecture has a test accuracy of 98.16%, and InceptionV3 has the lowest test accuracy of 91.66%. It has been concluded that DenseNet169 provides better results for the classification of brain tumors than other models.

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Analysing X-Ray Images to Detect Lung Diseases Using DenseNet-169 technique

Cited by 15 publications

References 5 publications

MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model

MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model

Chest X-ray Image Analysis using Convolutional Vision Transformer

Brain Tumor Detection Using Convolutional Neural Networks: A Comparative Study

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