Chest Diseases Prediction from X-ray Images using CNN Models: A Study

Mangeri, Latheesh; S, Gnana Prakasi O; Puppala, Neeraj; Kanmani, P

doi:10.14569/ijacsa.2021.0121026

Cited by 8 publications

(2 citation statements)

References 17 publications

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“…“.ipynb” files are simple, documented JSON files. It was chosen because it was accessed through a web browser, allowing the same interface to run locally as a desktop application and on a remote server [ 14 , 15 ].…”

Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

Badr¹,

Al-Otaibi

Alturki

et al. 2022

BioMed Research International

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X-ray images aid medical professionals in the diagnosis and detection of pathologies. They are critical, for example, in the diagnosis of pneumonia, the detection of masses, and, more recently, the detection of COVID-19-related conditions. The chest X-ray is one of the first imaging tests performed when pathology is suspected because it is one of the most accessible radiological examinations. Deep learning-based neural networks, particularly convolutional neural networks, have exploded in popularity in recent years and have become indispensable tools for image classification. Transfer learning approaches, in particular, have enabled the use of previously trained networks’ knowledge, eliminating the need for large data sets and lowering the high computational costs associated with this type of network. This research focuses on using deep learning-based neural networks to detect anomalies in chest X-rays. Different convolutional network-based approaches are investigated using the ChestX-ray14 database, which contains over 100,000 X-ray images with labels relating to 14 different pathologies, and different classification objectives are evaluated. Starting with the pretrained networks VGG19, ResNet50, and Inceptionv3, networks based on transfer learning are implemented, with different schemes for the classification stage and data augmentation. Similarly, an ad hoc architecture is proposed and evaluated without transfer learning for the classification objective with more examples. The results show that transfer learning produces acceptable results in most of the tested cases, indicating that it is a viable first step for using deep networks when there are not enough labeled images, which is a common problem when working with medical images. The ad hoc network, on the other hand, demonstrated good generalization with data augmentation and an acceptable accuracy value. The findings suggest that using convolutional neural networks with and without transfer learning to design classifiers for detecting pathologies in chest X-rays is a good idea.

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Section: Methodsmentioning

confidence: 99%

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

Badr¹,

Al-Otaibi

Alturki

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…In study [11] it is emphasized the how important are the predictions related to the X-ray images. The paper focus in chest diseases like tuberculosis (TB), COVID-19, and pneumonia.…”

Section: Literature Reviewmentioning

confidence: 99%

Studying the Behavior of a Modified Deep Learning Model for Disease Detection Through X-ray Chest Images

Zanaj,

Balliu,

Basha

et al. 2024

ijacsa

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In modern medical diagnostics, Deep Learning models are commonly used for illness diagnosis, especially over Xray chest images. Deep Learning approaches provide unmatched promise for early identification, prognosis, and treatment evaluation across a range of illnesses, by combining sophisticated algorithms with large datasets. It is crucial to research these models to lead to improved ones to progress toward disease identification's precision, effectiveness, and scalability. This paper presents the study of a CNN+VGG19 Deep Learning architecture (subsets of machine learning), both before and after its modification. The same dataset is used over the existing and modified models to compare metrics under the same conditions. They are compared using metrics like loss, accuracy, precision, sensitivity, and AUC. These metrics display lower values in the updated model than in the original one. The numbers demonstrate the occurrence of the overfitting phenomenon, which is most likely the result of the model's increased complexity for a small dataset. The noise in the images included in the dataset may also be the cause. As a result, it can be stated that regularization techniques should be applied; otherwise, layers of extraction and classification should not be added to the model to prevent overfitting.

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AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects

Asif,

Zhao,

et al. 2024

Arch Computat Methods Eng

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Chest Diseases Prediction from X-ray Images using CNN Models: A Study

Cited by 8 publications

References 17 publications

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

Deep Learning-Based Networks for Detecting Anomalies in Chest X-Rays

Studying the Behavior of a Modified Deep Learning Model for Disease Detection Through X-ray Chest Images

AI-Based Approaches for the Diagnosis of Mpox: Challenges and Future Prospects

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