Classification of benign-malignant pulmonary lung nodules using ensemble learning classifiers

Upadhyay, Sudhaker; Tanwar, Prakash Singh

doi:10.1109/icces51350.2021.9489012

Cited by 3 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In contrast to deep neural networks (CNNs) which use 2D layers, ANNs refer to multiple 1D layers of neurons stacked on top of each other for classification of features. They are also commonly known as multi-layer perceptrons, or feed-forward neural networks, and have been successfully used in medical image classification such as classifying CT scans containing lung nodules [30] and skin lesion malignancy [31]. For the current study, the ANN was implemented in Python’s Keras library, using an input layer with the same length as each row of features (560), 5 hidden Dense layers of 550 neurons each, and a Dense-3 layer with softmax activation at the output, as illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

Kuzinkovas

Clement

2022

Preprint

View full text Add to dashboard Cite

Advances in the field of image classification using convolutional neural networks (CNNs) have greatly improved the accuracy of medical image diagnosis by radiologists. Numerous research groups have applied CNN methods to diagnose respiratory illnesses from chest x-rays, and have extended this work to prove the feasibility of rapidly diagnosing COVID-19 to high degrees of accuracy. One issue in previous research has been the use of datasets containing only a few hundred images of chest x-rays containing COVID-19, causing CNNs to overfit the image data. This leads to a lower accuracy when the model attempts to classify new images, as would be clinically expected of it. In this work, we present a model trained on the COVID-QU-Ex dataset, overall containing 33,920 chest x-ray images, with an equal share of COVID-19, Non-COVID pneumonia, and Normal images. The model itself is an ensemble of pre-trained CNNs (ResNet50, VGG19, VGG16) and GLCM textural features. It achieved a 98.34% binary classification accuracy (COVID-19/no COVID-19) on a balanced test dataset of 6581 chest x-rays, and 94.68% for distinguishing between COVID-19, Non-COVID pneumonia and normal chest x-rays. Also, we herein discuss the effects of dataset size, demonstrating that a 98.82% 3-class accuracy can be achieved using the model if the training dataset only contains a few thousand images, but that generalisability of the model suffers with such small datasets.

show abstract

Section: Methodsmentioning

confidence: 99%

The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

Kuzinkovas

Clement

2022

Preprint

View full text Add to dashboard Cite

show abstract

Pulmonary Chest Nodule Detection through Adaptive Reinforcement Learning Model (ARLM)

Balajee

Haritha

Reddy

et al. 2023

2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)

View full text Add to dashboard Cite

The Detection of COVID-19 in Chest X-rays Using Ensemble CNN Techniques

Kuzinkovas

Clement

2023

Information

View full text Add to dashboard Cite

Advances in the field of image classification using convolutional neural networks (CNNs) have greatly improved the accuracy of medical image diagnosis by radiologists. Numerous research groups have applied CNN methods to diagnose respiratory illnesses from chest X-rays and have extended this work to prove the feasibility of rapidly diagnosing COVID-19 with high degrees of accuracy. One issue in previous research has been the use of datasets containing only a few hundred images of chest X-rays containing COVID-19, causing CNNs to overfit the image data. This leads to lower accuracy when the model attempts to classify new images, as would be clinically expected. In this work, we present a model trained on the COVID-QU-Ex dataset containing 33,920 chest X-ray images, with an equal share of COVID-19, Non-COVID pneumonia, and Normal images. The model is an ensemble of pre-trained CNNs (ResNet50, VGG19, and VGG16) and GLCM textural features. The model achieved a 98.34% binary classification accuracy (COVID-19/no COVID-19) on a test dataset of 6581 chest X-rays and 94.68% for distinguishing between COVID-19, Non-COVID pneumonia, and normal chest X-rays. The results also demonstrate that a higher 98.82% three-class test accuracy can be achieved using the model if the training dataset only contains a few thousand images. However, the generalizability of the model suffers due to the smaller dataset size. This study highlights the benefits of both ensemble CNN techniques and larger dataset sizes for medical image classification performance.

show abstract

Classification of benign-malignant pulmonary lung nodules using ensemble learning classifiers

Cited by 3 publications

References 21 publications

The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

The Detection of COVID-19 in Chest X-Rays Using Ensemble CNN Techniques

Pulmonary Chest Nodule Detection through Adaptive Reinforcement Learning Model (ARLM)

The Detection of COVID-19 in Chest X-rays Using Ensemble CNN Techniques

Contact Info

Product

Resources

About