Evaluating Deep Neural Network Architectures with Transfer Learning for Pneumonitis Diagnosis
Surya Krishnamurthy,Kathiravan Srinivasan,Saeed Mian Qaisar,P. M. Durai Raj Vincent,Chuan-Yu Chang +4 more
TLDR
In this paper, the authors compared various image classification models based on transfer learning with well-known deep learning architectures for pneumonitis classification from chest X-ray images and observed that the DenseNet201 model outperformed other models with an AUROC score of 0.966 and a recall score of0.99.Abstract:
Pneumonitis is an infectious disease that causes the inflammation of the air sac. It can be life-threatening to the very young and elderly. Detection of pneumonitis from X-ray images is a significant challenge. Early detection and assistance with diagnosis can be crucial. Recent developments in the field of deep learning have significantly improved their performance in medical image analysis. The superior predictive performance of the deep learning methods makes them ideal for pneumonitis classification from chest X-ray images. However, training deep learning models can be cumbersome and resource-intensive. Reusing knowledge representations of public models trained on large-scale datasets through transfer learning can help alleviate these challenges. In this paper, we compare various image classification models based on transfer learning with well-known deep learning architectures. The Kaggle chest X-ray dataset was used to evaluate and compare our models. We apply basic data augmentation and fine-tune our feed-forward classification head on the models pretrained on the ImageNet dataset. We observed that the DenseNet201 model outperforms other models with an AUROC score of 0.966 and a recall score of 0.99. We also visualize the class activation maps from the DenseNet201 model to interpret the patterns recognized by the model for prediction.read more
Citations
More filters
Journal ArticleDOI
Transformers in Medical Image Analysis: A Review
Kelei He,Chen Gan,Zhuoyuan Li,Islem Rekik,Zihao Yin,Wen Ji,Yang Gao,Junfeng Zhang,Dinggang Shen +8 more
TL;DR: In this article , the authors provide an overview of the core concepts of the attention mechanism built into transformers and other basic components, and review various transformer architectures tailored for medical image applications and discuss their limitations.
Journal ArticleDOI
Vision Transformer-based recognition of diabetic retinopathy grade
TL;DR: In this paper, a Transformer-based method was proposed to recognize the grade of diabetic retinopathy, which achieved an accuracy of 91.4%, specificity = 0.928-95% CI(0.852-1), sensitivity =0.863-0.989, Quadratic weighted kappa score (QWK) was 0. 935, and AUC = 0986.
Journal ArticleDOI
Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images
TL;DR: In this article , the authors proposed two ensemble learning techniques: E-CNN (product rule) and E-convolutional neural network (E-CNN) to classify colon cancer histopathology images into various classes.
Journal ArticleDOI
Mathematical analysis of the dynamics of COVID‐19 in Africa under the influence of asymptomatic cases and re‐infection
TL;DR: It is indicated that increasing case detection to detect asymptomatically infected individuals will be very effective in containing and reducing the burden of COVID‐19 in Africa.
Journal ArticleDOI
Deep learning-based pancreas volume assessment in individuals with type 1 diabetes
Raphael Roger,Melissa A. Hilmes,Jonathan M. Williams,Daniel J. Moore,Alvin C. Powers,R. Cameron Craddock,John Virostko +6 more
TL;DR: In this article , a convolutional neural network was trained using manual pancreas annotation on 160 abdominal magnetic resonance imaging (MRI) scans from individuals with T1D, controls, or a combination thereof.
References
More filters
Journal ArticleDOI
A survey on deep learning in medical image analysis
Geert Litjens,Thijs Kooi,Babak Ehteshami Bejnordi,Arnaud Arindra Adiyoso Setio,Francesco Ciompi,Mohsen Ghafoorian,Jeroen van der Laak,Bram van Ginneken,Clara I. Sánchez +8 more
TL;DR: This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.
Journal ArticleDOI
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
Xiaoxuan Liu,Livia Faes,Aditya Kale,Siegfried K Wagner,Dun Jack Fu,Alice Bruynseels,Thushika Mahendiran,Gabriella Moraes,Mohith Shamdas,Christoph Kern,Christoph Kern,Joseph R. Ledsam,Martin Schmid,Konstantinos Balaskas,Konstantinos Balaskas,Eric J. Topol,Lucas M. Bachmann,Pearse A. Keane,Alastair K Denniston +18 more
TL;DR: A major finding of the review is that few studies presented externally validated results or compared the performance of deep learning models and health-care professionals using the same sample, which limits reliable interpretation of the reported diagnostic accuracy.
Journal ArticleDOI
Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.
Stephanie Harmon,Thomas Sanford,Sheng Xu,Evrim B. Turkbey,Holger R. Roth,Ziyue Xu,Dong Yang,Andriy Myronenko,Victoria L. Anderson,Amel Amalou,Maxime Blain,Michael T. Kassin,Dilara Long,Nicole Varble,Nicole Varble,Stephanie M. Walker,Ulas Bagci,Anna Maria Ierardi,Elvira Stellato,Guido Giovanni Plensich,Giuseppe Franceschelli,Cristiano Girlando,Giovanni Irmici,Dominic Labella,Dima A. Hammoud,Ashkan A. Malayeri,Elizabeth C. Jones,Ronald M. Summers,Peter L. Choyke,Daguang Xu,Mona Flores,Kaku Tamura,Hirofumi Obinata,Hitoshi Mori,Francesca Patella,Maurizio Cariati,Gianpaolo Carrafiello,Gianpaolo Carrafiello,Peng An,Bradford J. Wood,Baris Turkbey +40 more
TL;DR: It is shown that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.8% accuracy, with 84% sensitivity and 93% specificity.
Journal ArticleDOI
An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare.
TL;DR: It is difficult to obtain a large amount of pneumonia dataset for this classification task, so several data augmentation algorithms were deployed to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.
Related Papers (5)
Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems.
Saleh Albahli,Waleed Albattah +1 more