Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

Stirenko, Sergii; Kochura, Yuriy; Alienin, Oleg; Rokovyi, Oleksandr; Gordienko, Yuri; Peng, Gang; Zeng, Wei

doi:10.1109/elnano.2018.8477564

Cited by 113 publications

(81 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, many doctors and radiologists use an X-ray film to diagnose lung disease. As these are time taking, medical personnel are unable to respond for medical treatment on time [6]. Therefore, the use of CAD system can help medical screening to reduce the duty of medical personnel.…”

Section: Problem Definitionmentioning

confidence: 99%

“…3. The recall rate (Recall) correctly predicts the number of categories divided by the total number of data actually belonging to each category, as shown in Formula (6).…”

Section: Evaluating Cnn Model Performance For Lung Disease Predictionmentioning

confidence: 99%

“…The patients infected by lung disease are numerous and still increasing in number and it takes a great deal of time for the doctor to diagnosis the disease due to a lack of radiologists. A computer-aided diagnosis (CAD) system is also used in lung disease screening [6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

2020

View full text Add to dashboard Cite

Image processing technologies and computer-aided diagnosis are medical technologies used to support decision-making processes of radiologists and medical professionals who provide treatment for lung disease. These methods involve using chest X-ray images to diagnose and detect lung lesions, but sometimes there are abnormal cases that take some time to occur. This experiment used 5810 images for training and validation with the MobileNet, Densenet-121 and Resnet-50 models, which are popular networks used to classify the accuracy of images, and utilized a rotational technique to adjust the lung disease dataset to support learning with these convolutional neural network models. The results of the convolutional neural network model evaluation showed that Densenet-121, with a state-of-the-art Mish activation function and Nadam-optimized performance. All the rates for accuracy, recall, precision and F1 measures totaled 98.88%. We then used this model to test 10% of the total images from the non-dataset training and validation. The accuracy rate was 98.97% for the result which provided significant components for the development of a computer-aided diagnosis system to yield the best performance for the detection of lung lesions.

show abstract

Section: Problem Definitionmentioning

confidence: 99%

“…3. The recall rate (Recall) correctly predicts the number of categories divided by the total number of data actually belonging to each category, as shown in Formula (6).…”

Section: Evaluating Cnn Model Performance For Lung Disease Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

2020

View full text Add to dashboard Cite

show abstract

“…For all the images, the lesions have been manually annotated by expert dermatologists for normal skin and other miscellaneous structures. b) The Shenzhen Chest X-Ray Dataset [20], hereafter referred to as LUNG, consists of 662 frontal chest X-Ray images, out of which 336 are cases with manifestations of tuberculosis and the remaining 326 are non-diseased ones. The corresponding ground truth masks contain manually traced out boundaries for the left and the right lungs.…”

Section: Datamentioning

confidence: 99%

Improved Inference via Deep Input Transfer

Taghanaki

Abhishek

Hamarneh

2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Although numerous improvements have been made in the field of image segmentation using convolutional neural networks, the majority of these improvements rely on training with larger datasets, model architecture modifications, novel loss functions, and better optimizers. In this paper, we propose a new segmentation performance boosting paradigm that relies on optimally modifying the network's input instead of the network itself. In particular, we leverage the gradients of a trained segmentation network with respect to the input to transfer it to a space where the segmentation accuracy improves. We test the proposed method on three publicly available medical image segmentation datasets: the ISIC 2017 Skin Lesion Segmentation dataset, the Shenzhen Chest X-Ray dataset, and the CVC-ColonDB dataset, for which our method achieves improvements of 5.8%, 0.5%, and 4.8% in the average Dice scores, respectively.

show abstract

“…The X-rays were collected within a one-month period, mostly in September 2012, as a part of the daily routine, using a Philips DR Digital Diagnost system. The dataset was constructed by manually-segmented lung masks for the Shenzhen Hospital X-ray set as presented in [29]. These segmented lung masks were originally utilized for the description of the lung segmentation technique in combination with lossless and lossy data augmentation.…”

mentioning

confidence: 99%

A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays

et al. 2019

View full text Add to dashboard Cite

During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large repositories of classified (labeled) images and mostly of unclassified (unlabeled) images from human experts. Machine learning methods such as semi-supervised learning algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, by exploiting the explicit classification information of labeled data with the information hidden in the unlabeled data. In the present work, we propose a new ensemble semi-supervised learning algorithm for the classification of lung abnormalities from chest X-rays based on a new weighted voting scheme. The proposed algorithm assigns a vector of weights on each component classifier of the ensemble based on its accuracy on each class. Our numerical experiments illustrate the efficiency of the proposed ensemble methodology against other state-of-the-art classification methods.

show abstract

Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation

Cited by 113 publications

References 15 publications

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

Analyzing Lung Disease Using Highly Effective Deep Learning Techniques

Improved Inference via Deep Input Transfer

A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays

Contact Info

Product

Resources

About