Machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification

Yates, Elliot; Yates, Louise C.; Harvey, Hugh

doi:10.1016/j.crad.2018.05.015

Cited by 49 publications

(37 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep convolutional neural networks consist of an architecture that resembles the organization of the human brain and as such can be used to process and analyze data in a similar manner as humans. 9 Machine learning for image recognition in the field of dermatology has been previously examined. 10 The next step in this area is to develop methods to improve the image recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

Data augmentation in dermatology image recognition using machine learning

Aggarwal

2019

Skin Res Technol

View full text Add to dashboard Cite

Background: Each year in the United States, over 80 million people are affected by acne, atopic dermatitis, rosacea, psoriasis, and impetigo. Artificial intelligence and machine learning could prove to be a good tool for assisting in the diagnosis of dermatological conditions. The objective of this study was to evaluate the use of data augmentation in machine learning image recognition of five dermatological disease manifestations-acne, atopic dermatitis, impetigo, psoriasis, and rosacea. Materials and Methods:Open-source dermatological images were gathered and used to retrain TensorFlow Inception version-3. Retraining was done twice-once with and once without data augmentation. Both models were tested with the same images, and R software was used to perform statistical analysis. Results:The average of each of the statistical measures (sensitivity, specificity, PPV, NPN, MCC, and F1 Score) increased when data augmentation was added to the model. In particular, the average Matthews correlation coefficient increased by 7.7%.Each of the five dermatological manifestations had an increase in area under the curve (AUC) after data augmentation with the average increase in AUC of 0.132 and a standard deviation of 0.033. Atopic dermatitis had the highest increase in AUC of 0.18. With data augmentation, the lowest AUC was 0.87 for psoriasis and the highest was 0.97 for acne, indicating that the model performs well. Conclusion:With a deep learning-based approach, it is possible to differentiate dermatological images with appreciable MCC, F1 score, and AUC. Further, data augmentation can be used to increase the model's accuracy by a significant amount. K E Y W O R D Sacne, artificial intelligence, atopic dermatitis, image recognition, impetigo, psoriasis, rosacea How to cite this article: Aggarwal 1LP. Data augmentation in dermatology image recognition using machine learning. Skin

show abstract

Section: Introductionmentioning

confidence: 99%

Data augmentation in dermatology image recognition using machine learning

Aggarwal

2019

Skin Res Technol

View full text Add to dashboard Cite

show abstract

“…Before a case is even opened, AI may be able to triage abnormal cases to the top of the interpretation list. [24][25][26] In one study, AI was able to classify chest radiographs as normal or abnormal with an accuracy of 94.6%. 24 Another application was built to screen head CT scans for acute neurologic events, thus helping to facilitate workflow.…”

Section: Workflow and Image Interpretationmentioning

confidence: 99%

“…[24][25][26] In one study, AI was able to classify chest radiographs as normal or abnormal with an accuracy of 94.6%. 24 Another application was built to screen head CT scans for acute neurologic events, thus helping to facilitate workflow. 25 AI can also help to improve the way images are displayed so that interpreters need not waste time adjusting hanging protocols for examinations.…”

Section: Workflow and Image Interpretationmentioning

confidence: 99%

Artificial Intelligence in Radiology Residency Training

Forney

McBride

2020

Semin Musculoskelet Radiol

View full text Add to dashboard Cite

Artificial intelligence (AI) is an emerging technology that brings a wide array of new tools to the field of radiology. AI will certainly have an impact on the day-to-day work of radiologists in the coming decades, thus training programs must prepare radiology residents adequately for their future careers. Radiology training programs should aim to give residents an understanding of the fundamentals and types of AI in radiology, the broad areas AI can be applied in radiology, how to assess AI applications in radiology, and resources available to build their knowledge in IA applications in radiology.

show abstract

“…Xu et al [42] designed a hierarchical CNN CXNet-m1, which used a novel sin-loss loss function to learn from misclassified and indistinguishable images for anomaly identification on chest X-rays. Yates et al [43] retrained a final layer of the CNN model, Inception v3, and performed binary normality classification. In addition, da Nóbrega et al [44] compared the ResNet50 feature extractor combined with the SVM RBF classifier for recognition of the malignancy of a lung nodule.…”

Section: Introductionmentioning

confidence: 99%

A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

et al. 2020

View full text Add to dashboard Cite

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children's Medical Center dataset.

show abstract

Machine learning “red dot”: open-source, cloud, deep convolutional neural networks in chest radiograph binary normality classification

Cited by 49 publications

References 7 publications

Data augmentation in dermatology image recognition using machine learning

Data augmentation in dermatology image recognition using machine learning

Artificial Intelligence in Radiology Residency Training

A Novel Transfer Learning Based Approach for Pneumonia Detection in Chest X-ray Images

Contact Info

Product

Resources

About