Deep-learning based detection of eosinophilic esophagitis

Endoscopy ◽  
2021 ◽  
Author(s):  
Pedro Guimarães ◽  
Andreas Keller ◽  
Tobias Fehlmann ◽  
Frank Lammert ◽  
Markus Casper
Keyword(s):  
Deep Learning ◽  
Eosinophilic Esophagitis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Diagnostic Delay ◽  
Test Set ◽  
Candida Esophagitis ◽  
Endoscopic Classification ◽  
Classification Tasks ◽  
Global Accuracy

Background and aims: For eosinophilic esophagitis (EoE) a substantial diagnostic delay is still a clinically relevant phenomenon. Deep learning-based algorithms have demonstrated potential in medical image analysis. Here we establish a convolutional neuronal network (CNN)-based approach that can distinguish EoE from normal findings and candida esophagitis. Methods: We trained and tested a CNN using 484 real-world endoscopic images from 134 subjects consisting of three classes (normal, EoE, and candidiasis). Images were split into two completely independent datasets. The proposed approach was evaluated against three trainee endoscopists on the test set. Model-explainability was enhanced by deep Taylor decomposition. Results: Global accuracy (0.915 [0.880-0.940]), sensitivity (0.871 [0.819-0.910]) and specificity (0.936 [0.910-0.955]) were significantly higher than for endoscopists on the test set. Global area under the ROC curve was 0.966 [0.954-0.975]. Results were highly reproducible. Explainability analysis found that the algorithm identified characteristic signs also used by endoscopists. Conclusions: Complex endoscopic classification tasks including more than two classes can be solved by CNN-based algorithms. Thus, our algorithm (https://ccb-test.cs.uni-saarland.de/EoE/) may assist clinicians in making the diagnosis of EoE.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals Trends in the application of deep learning networks in medical image analysis: evolution between 2012 and 2020

2021 ◽  
pp. 110069
Author(s):  
Lu Wang ◽  
Hairui Wang ◽  
Yingna Huang ◽  
Baihui Yan ◽  
Zhihui Chang ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Learning Networks

scholarly journals Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

2020 ◽  
Vol 10 (2) ◽  
pp. 118 ◽  
Author(s):  
Muhammad Waqas Nadeem ◽  
Mohammed A. Al Ghamdi ◽  
Muzammil Hussain ◽  
Muhammad Adnan Khan ◽  
Khalid Masood Khan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Computational Models ◽  
Medical Image Analysis ◽  
Critical Discussion ◽  
Levels Of Abstraction ◽  
Discussion Section ◽  
Wide Range ◽  
Classification Prediction

Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.

scholarly journals Deep Convolution Neural Networks for Medical Image Analysis

2018 ◽  
Vol 7 (3.33) ◽  
pp. 115 ◽  
Author(s):  
Myung Jae Lim ◽  
Da Eun Kim ◽  
Dong Kun Chung ◽  
Hoon Lim ◽  
Young Man Kwon
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Early Detection ◽  
Medical Image ◽  
Medical Images ◽  
Medical Image Analysis ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Histopathological Image ◽  
Approach Method

Breast cancer is a highly contagious disease that has killed many people all over the world. It can be fully recovered from early detection. To enable the early detection of the breast cancer, it is very important to classify accurately whether it is breast cancer or not. Recently, the deep learning approach method on the medical images such as these histopathologic images of the breast cancer is showing higher level of accuracy and efficiency compared to the conventional methods. In this paper, the breast cancer histopathological image that is difficult to be distinguished was analyzed visually. And among the deep learning algorithms, the CNN(Convolutional Neural Network) specialized for the image was used to perform comparative analysis on whether it is breast cancer or not. Among the CNN algorithms, VGG16 and InceptionV3 were used, and transfer learning was used for the effective application of these algorithms.The data used in this paper is breast cancer histopathological image dataset classifying the benign and malignant of BreakHis. In the 2-class classification task, InceptionV3 achieved 98% accuracy. It is expected that this deep learning approach method will support the development of disease diagnosis through medical images.  

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