scholarly journals Comparative Study of Transfer Learning Models for Retinal Disease Diagnosis from Fundus Images

2022 ◽  
Vol 70 (3) ◽  
pp. 5821-5834
Author(s):  
Kuntha Pin ◽  
Jee Ho Chang ◽  
Yunyoung Nam
Keyword(s):  
Comparative Study ◽  
Transfer Learning ◽  
Retinal Disease ◽  
Disease Diagnosis ◽  
Fundus Images ◽  
Learning Models

scholarly journals A Comparative Study of Alzheimer’s Disease Classification using Multiple Transfer Learning Models

2019 ◽  
Vol 6 (4) ◽  
pp. 209-216
Author(s):  
Deekshitha Prakash ◽  
Nuwan Madusanka ◽  
Subrata Bhattacharjee ◽  
Hyeon-Gyun Park ◽  
Cho-Hee Kim ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Comparative Study ◽  
Transfer Learning ◽  
Disease Classification ◽  
Learning Models

scholarly journals A Comparative Study of Deep Learning Models for Diagnosing Glaucoma From Fundus Images

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 23894-23906
Author(s):  
Manal Alghamdi ◽  
Mohamed Abdel-Mottaleb
Keyword(s):  
Deep Learning ◽  
Comparative Study ◽  
Fundus Images ◽  
Learning Models

Deep Learning in Disease Diagnosis: Models and Datasets

2020 ◽  
Vol 15 ◽  
Author(s):  
Deeksha Saxena ◽  
Mohammed Haris Siddiqui ◽  
Rajnish Kumar
Keyword(s):  
Biological Sciences ◽  
Machine Learning ◽  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Data Types ◽  
Related Data ◽  
Abstract Level ◽  
Experimental Validations ◽  
Selection Of

Background: Deep learning (DL) is an Artificial neural network-driven framework with multiple levels of representation for which non-linear modules combined in such a way that the levels of representation can be enhanced from lower to a much abstract level. Though DL is used widely in almost every field, it has largely brought a breakthrough in biological sciences as it is used in disease diagnosis and clinical trials. DL can be clubbed with machine learning, but at times both are used individually as well. DL seems to be a better platform than machine learning as the former does not require an intermediate feature extraction and works well with larger datasets. DL is one of the most discussed fields among the scientists and researchers these days for diagnosing and solving various biological problems. However, deep learning models need some improvisation and experimental validations to be more productive. Objective: To review the available DL models and datasets that are used in disease diagnosis. Methods: Available DL models and their applications in disease diagnosis were reviewed discussed and tabulated. Types of datasets and some of the popular disease related data sources for DL were highlighted. Results: We have analyzed the frequently used DL methods, data types and discussed some of the recent deep learning models used for solving different biological problems. Conclusion: The review presents useful insights about DL methods, data types, selection of DL models for the disease diagnosis.

Crop Disease Diagnosis using Deep Learning Models

2020 ◽  
Author(s):  
Waleej Haider ◽  
Aqeel Ur Rehman ◽  
Ahmed Maqsood ◽  
Syed Zurain Javed
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Learning Models ◽  
Crop Disease

scholarly journals A Comparative Study for Efficient Covid-19 Detecting Machine Learning Models on CT Images

2021 ◽  
Vol 1099 (1) ◽  
pp. 012056
Author(s):  
Ankur Chaturvedi ◽  
Divyansh Mishra ◽  
Dr. Vikram Rajpoot ◽  
Janvi Gupta ◽  
Aditi Sharma
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Ct Images ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images

2021 ◽  
Vol 11 (9) ◽  
pp. 4233
Author(s):  
Biprodip Pal ◽  
Debashis Gupta ◽  
Md. Rashed-Al-Mahfuz ◽  
Salem A. Alyami ◽  
Mohammad Ali Moni
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Chest Radiography ◽  
High Sensitivity ◽  
Ct Images ◽  
Classification Performance ◽  
Learning Models ◽  
X Ray ◽  
Image Capturing ◽  
Fast Gradient

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

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