Detection of Pneumonia using ML &amp; DL in Python

Sharma, Arjun; Negi, Monika; Goyal, Ankush; Jain, Rachna; Nagrath, Preeti

doi:10.1088/1757-899x/1022/1/012066

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…The ensembled model evaluated had an accuracy of 95.03 and AUC score of 94.5 with a precision of 96.92. Sharma [7] developed straightforward CNN structures for categorizing chest X-ray images indicative of pneumonia. They implemented data augmentation to address the limited data availability and achieved a classification accuracy of 90.68% on the dataset supplied by Kermany et al, hereafter called the Kermany dataset.…”

Section: Literature Reviewmentioning

confidence: 99%

Pneumonia Detection using CNN, Resnet and DenseNet

Gundabatini

2024

IJRASET

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Pneumonia is a prevalent respiratory infection that poses a significant threat to public health. Timely and precise diagnosis is essential for effective treatment and patient management. The proposed methodology involves training a CNN (Convolutional Neural Networks), ResNet-50 and DenseNet-121, on a large dataset of chest X ray images. CNNs play a crucial role in feature extraction from chest X-ray images for various computer vision tasks, including image recognition. The CNN model automatically learns and extracts essential visual features from the input images, capturing patterns and characteristics. On the other hand, ResNet-50 and DenseNet-121 leverage their effectiveness in handling deeper architectures and handling vanishing gradient problems. We compare our approach with existing methods to assess the quality and accuracy of the generated captions. The models undergo training and testing utilizing an extensive dataset comprising chest X-ray images, demonstrating high accuracy in detecting pneumonia, potentially offering a valuable tool for early diagnosis and treatment. The proposed pneumonia detection framework holds great promise for assisting healthcare professionals in diagnosing and treating pneumonia, thereby improving patient outcomes, and reducing healthcare costs.

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Section: Literature Reviewmentioning

confidence: 99%

Pneumonia Detection using CNN, Resnet and DenseNet

Gundabatini

2024

IJRASET

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“…A Sharma et .al [22] introduced, 'Detection of Pneumonia using ML & DL in Python' the methodology and approach has been explained. All of the X-ray images were trimmed to the ideal sizes for calculating by preprocessing the data.…”

Section: Deep Learningmentioning

confidence: 99%

Pneumonia Detection using Chest X-Ray Images and Machine Learning Techniques

Choubey

2023

IJSREM

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Abstract— Chest X-rays have been a crucial diagnostic tool in detecting pneumonia. The disease manifests as inflammation and filling of the air spaces within the lungs, leading to changes in the appearance of the lungs on X-rays. In recent times, advances in computer vision and machine learning have allowed for the development of automated systems for pneumonia detection using chest X-rays. These systems leverage deep learning techniques to analyze X-ray images and identify patterns associated with pneumonia. The use of these systems can aid in early diagnosis and prompt treatment, potentially leading to improved patient outcomes. Despite their promise, there are still challenges to be addressed, including the need for large and diverse datasets for training and the need for further validation of these systems in real-world settings. Nevertheless, the development of automated pneumonia detection systems using chest X-rays has the potential to revolutionize the field of radiology and improve patient care. Index Terms—Pneumonia; artificial intelligence; machine learning; image processing; convolutional neural network; chest x-rays.

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“…The study [31] employed fine-tuned parameters and information obtained via transfer learning to develop ensemble learning models for COVID-19 identification from chest X-rays. Another similar study is [32], which proposed a CNN-based deep learning model. Images are resized and preprocessed for better results.…”

Section: Literature Reviewmentioning

confidence: 99%

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

et al. 2022

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Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung’s tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen’s kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively.

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Detection of Pneumonia using ML & DL in Python

Cited by 7 publications

References 6 publications

Pneumonia Detection using CNN, Resnet and DenseNet

Pneumonia Detection using CNN, Resnet and DenseNet

Pneumonia Detection using Chest X-Ray Images and Machine Learning Techniques

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

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