RETRACTED ARTICLE: Lung nodule malignancy
            classification in chest computed tomography images using transfer learning and
            convolutional neural networks

Nóbrega, Raul Victor Medeiros da; Filho, Pedro Pedrosa Rebouças; Rodrigues, Murillo Barata; Silva, Suane Pires Pinheiro da; Júnior, Carlos M. J. M. Dourado; Albuquerque, Victor Hugo C. de

doi:10.1007/s00521-018-3895-1

Cited by 77 publications

(51 citation statements)

References 42 publications

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“…The transfer learning approach is mostly used to work around computational costs of training a network from scratch or to keep the feature extractor trained during the first task. In medical applications, the most accepted practice of transfer learning is to utilize the CNNs that achieved the best results in the ImageNet large scale visual recognition challenge (ILSVRC) [24], which assesses algorithms for object detection and classification in large scales. The use of large datasets for initial training of the network enables high performance in smaller datasets.…”

Section: Transfer Learning With Convolutional Neural Networkmentioning

confidence: 99%

Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

Ohata

Bezerra

Chagas

et al. 2021

IEEE/CAA J. Autom. Sinica

216

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The new coronavirus (COVID-19), declared by the World Health Organization as a pandemic, has infected more than 1 million people and killed more than 50 thousand. An infection caused by COVID-19 can develop into pneumonia, which can be detected by a chest X-ray exam and should be treated appropriately. In this work, we propose an automatic detection method for COVID-19 infection based on chest X-ray images. The datasets constructed for this study are composed of 194 X-ray images of patients diagnosed with coronavirus and 194 X-ray images of healthy patients. Since few images of patients with COVID-19 are publicly available, we apply the concept of transfer learning for this task. We use different architectures of convolutional neural networks (CNNs) trained on ImageNet, and adapt them to behave as feature extractors for the X-ray images. Then, the CNNs are combined with consolidated machine learning methods, such as k-Nearest Neighbor, Bayes, Random Forest, multilayer perceptron (MLP), and support vector machine (SVM). The results show that, for one of the datasets, the extractor-classifier pair with the best performance is the MobileNet architecture with the SVM classifier using a linear kernel, which achieves an accuracy and an F1-Score of 98.5%. For the other dataset, the best pair is DenseNet201 with MLP, achieving an accuracy and an F1-Score of 95.6%. Thus, the proposed approach demonstrates efficiency in detecting COVID-19 in X-ray images.

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Section: Transfer Learning With Convolutional Neural Networkmentioning

confidence: 99%

Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

Ohata

Bezerra

Chagas

et al. 2021

IEEE/CAA J. Autom. Sinica

216

View full text Add to dashboard Cite

show abstract

“…After this process, a pre-trained model does not behave as a classifier, so it is used as a feature extractor. The transfer learning technique is detailed in the work of da Nóbrega et al ( 2018 ), who applied transfer learning to lung nodule classification.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals

Zhang

Silva

Ohata

et al. 2020

Front. Hum. Neurosci.

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“…Furthermore, lung features extracted from chest X-ray images show that they help CAD systems immensely in detecting abnormalities and serious diseases like pneumoconiosis. Especially, the pre-trained feature extractor "ResNet50" [21] was exploited with some classifiers such as SVM and CNN for detecting and classifying lung nodule disease in chest CT images. In [22], some irregular features like the shape and size of lungs were extracted with the gray level co-occurrence matrix (GLCM) and then used within the probabilistic neural network (PNN) classifier to accurately classify chest radiographs into the normal or abnormal category.…”

Section: Related Workmentioning

confidence: 99%

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

2020

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The accurate detection of abnormalities in medical images (like X-ray and CT scans) is a challenging problem due to images’ blurred boundary contours, different sizes, variable shapes, and uneven density. In this paper, we tackle this problem via a new effective online variational learning model for both mixtures of finite and infinite Gamma distributions. The proposed approach takes advantage of the Gamma distribution flexibility, the online learning scalability, and the variational inference efficiency. Three different batch and online learning methods based on robust texture-based feature extraction are proposed. Our work is evaluated and validated on several real challenging data sets for different kinds of pneumonia infection detection. The obtained results are very promising given that we approach the classification problem in an unsupervised manner. They also confirm the superiority of the Gamma mixture model compared to the Gaussian mixture model for medical images’ classification.

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RETRACTED ARTICLE: Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks

Cited by 77 publications

References 42 publications

Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

Automatic detection of COVID-19 infection using chest X-ray images through transfer learning

Bi-Dimensional Approach Based on Transfer Learning for Alcoholism Pre-disposition Classification via EEG Signals

Online Learning of Finite and Infinite Gamma Mixture Models for COVID-19 Detection in Medical Images

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