Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Suryaa, V S; R, Arockia Xavier Annie; Aiswarya, M.

doi:10.14569/ijacsa.2021.0121084

Cited by 7 publications

(4 citation statements)

References 17 publications

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“…The model could detect pneumonia with 90% accuracy. The study sV S Suryaa [6] presents a transfer-learning based ensemble model to automate Pneumonia detection using Chest X-rays. Different CNN architectures were fine-tuned, trained and the results analysed to finally propose an ensemble model.…”

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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“…The construction of models was facilitated through the utilization of two primary implementation libraries: Keras and Autokeras. Additionally, the Adam optimizer and cross-entropy loss function [41], were employed for training all algorithms. Table II presents the hyperparameters used for fine-tuning the pre-trained models utilized in this research.…”

Section: A Implementation Platformmentioning

confidence: 99%

Enhancing Skin Diseases Classification Through Dual Ensemble Learning and Pre-trained CNNs

Gannour¹,

Hamida²,

Lamalem³

et al. 2023

IJACSA

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Skin diseases represent a variety of disorders that can affect the skin. In fact, early diagnosis plays a central role in the treatment of this type of disease. This scholarly article introduces a novel approach to classifying skin diseases by leveraging two ensemble learning techniques, encompassing multi-modal and multi-task methodologies. The proposed classifier integrates diverse information sources, including skin lesion images and patient-specific data, aiming to enhance the accuracy of disease classification. By simultaneously utilizing image input and structured data input, the multi-task functionality of the classifier enables efficient disease classification. The integration of multi-modal and multi-task techniques allows for a comprehensive analysis of skin diseases, leading to improved classification performance and a more holistic understanding of the underlying factors influencing disease diagnosis. The efficacy of the classifier was assessed using the ISIC 2018 dataset, which comprises both image and clinical information for each patient with skin diseases. The dataset used in this study comprises images of seven different types of skin diseases and their associated medical information. The findings of our proposed approach show that it outperforms traditional single-modal and single-task classifiers. The results of this study demonstrate that the proposed model attained an accuracy of 97.66% for the initial classification task (image classification). Additionally, the second classification task (clinical data classification) achieved an accuracy of 94.40%.

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“…However, the model was limited to considering only one type of lung disease. Suryaa S. V (10) developed an ensemble learning technique using VGG-16, ResNet-50, ResNet-101, ResNet-152, and VGG-19 for pneumonia diagnosis. Hashmi F. M. et al built a weighted classifier using ResNet-18, Xception, InceptionV3, DenseNet121, and MobileNetV3 to identify pneumonia.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hypertuned DNN Based Approach for Pneumonia Detection

Salkade,

Rathi

2024

IJST

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Objectives: To create a deep learning system for pneumonia detection that is both effective and gradually optimized. Methods: A customized CNN is used with an incremental approach for pneumonia classification and detection. Starting with a baseline model, hypertuning parameters such as four convolution layers with filters of 16, 32, 64, and 128 sizes, a dropout layer with values of 0.3, 0.5, and 0.7, four batch normalization layers, and an Adam optimizer are added. A total images of 5,863 for training, 624 for testing, and 16 for validation from the Paul Mooney dataset were used to test the suggested model. Findings: The study recorded a test accuracy of 94% for the customized CNN followed by ResNet50 at 79.9%, VGG16 at 90.14%, VGG19 at 82.21%, InceptionV3 at 74.51%, and EfficientNetB1 at 83.17%. Recall of 98.20%, accuracy of 85.55%, AUC of 93.52%, and F1_score of 92.45% obtained were all fairly excellent for the customized CNN. 15 epochs, a learning rate of 0.0001, callbacks with a patience of 3, and an early stopping feature were applied to the training model. Novelty: Five convolution blocks, two separable convolution layers, one batch normalization layer, one maxpooling layer, and a fully connected layer with an Adam optimizer were all included in the customized CNN that was developed to identify and categorize pneumonia. With Explainable AI's GradCAM technology, pneumonia-infected areas on chest X-rays were highlighted and the sickness was seen. Keywords: Customized CNN, VGG16, VGG19, ResNet50, Explainable AI

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Efficient DNN Ensemble for Pneumonia Detection in Chest X-ray Images

Cited by 7 publications

References 17 publications

Pneumonia Detection using CNN, Resnet and DenseNet

Pneumonia Detection using CNN, Resnet and DenseNet

Enhancing Skin Diseases Classification Through Dual Ensemble Learning and Pre-trained CNNs

An Efficient Hypertuned DNN Based Approach for Pneumonia Detection

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