scholarly journals Object or Background: An Interpretable Deep Learning Model for Covid-19 Detection from CT-Scan Images

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1732
Author(s):  
Gurmail Singh ◽  
Kin-Choong Yow
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Learning Model ◽  
Chest Ct ◽  
High Stake ◽  
Reasoning Process ◽  
Economic Problems ◽  
Learning Techniques ◽  
Deep Learning Model ◽  
Chest Ct Scan

The new strains of the pandemic Covid-19 are still looming. It is important to develop multiple approaches for timely and accurate detection of Covid-19 and its variants. Deep learning techniques are well proved for their efficiency in providing solutions to many social and economic problems. However, the transparency of the reasoning process of a deep learning model related to a high stake decision is a necessity. In this work, we propose an interpretable deep learning model Ps-ProtoPNet to detect Covid-19 from the medical images. Ps-ProtoPNet classifies the images by recognizing the objects rather than their background in the images. We demonstrate our model on the dataset of the chest CT-scan images. The highest accuracy that our model achieves is 99.29%

scholarly journals Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nathalie Lassau ◽  
Samy Ammari ◽  
Emilie Chouzenoux ◽  
Hugo Gortais ◽  
Paul Herent ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Learning Model ◽  
Ct Scans ◽  
Clinical Variables ◽  
Biological Variables ◽  
Severity Scores ◽  
Scan Data ◽  
Deep Learning Model ◽  
Chest Ct Scan

AbstractThe SARS-COV-2 pandemic has put pressure on intensive care units, so that identifying predictors of disease severity is a priority. We collect 58 clinical and biological variables, and chest CT scan data, from 1003 coronavirus-infected patients from two French hospitals. We train a deep learning model based on CT scans to predict severity. We then construct the multimodal AI-severity score that includes 5 clinical and biological variables (age, sex, oxygenation, urea, platelet) in addition to the deep learning model. We show that neural network analysis of CT-scans brings unique prognosis information, although it is correlated with other markers of severity (oxygenation, LDH, and CRP) explaining the measurable but limited 0.03 increase of AUC obtained when adding CT-scan information to clinical variables. Here, we show that when comparing AI-severity with 11 existing severity scores, we find significantly improved prognosis performance; AI-severity can therefore rapidly become a reference scoring approach.

Explainable Deep Learning Model for COVID-19 Screening in Chest CT Images

2021 ◽  
pp. 981-990
Author(s):  
Mostafa El Habib Daho ◽  
Amin Khouani ◽  
Mohammed El Amine Lazouni ◽  
Sidi Ahmed Mahmoudi
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Ct Images ◽  
Chest Ct ◽  
Deep Learning Model

scholarly journals A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images

2021 ◽  
pp. 105014
Author(s):  
Khabir Uddin Ahamed ◽  
Manowarul Islam ◽  
Ashraf Uddin ◽  
Arnisha Akhter ◽  
Bikash Kumar Paul ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
Learning Approach ◽  
X Ray ◽  
Chest Ct Scan

scholarly journals Deep learning model for classification and bioactivity prediction of essential oil-producing plants from Egypt

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Noha E. El-Attar ◽  
Mohamed K. Hassan ◽  
Othman A. Alghamdi ◽  
Wael A. Awad
Keyword(s):  
Deep Learning ◽  
Essential Oil ◽  
Biological Activities ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Chemical Compositions ◽  
Linear Processes ◽  
Learning Techniques ◽  
Important Trend ◽  
Deep Learning Model

AbstractReliance on deep learning techniques has become an important trend in several science domains including biological science, due to its proven efficiency in manipulating big data that are often characterized by their non-linear processes and complicated relationships. In this study, Convolutional Neural Networks (CNN) has been recruited, as one of the deep learning techniques, to be used in classifying and predicting the biological activities of the essential oil-producing plant/s through their chemical compositions. The model is established based on the available chemical composition’s information of a set of endemic Egyptian plants and their biological activities. Another type of machine learning algorithms, Multiclass Neural Network (MNN), has been applied on the same Essential Oils (EO) dataset. This aims to fairly evaluate the performance of the proposed CNN model. The recorded accuracy in the testing process for both CNN and MNN is 98.13% and 81.88%, respectively. Finally, the CNN technique has been adopted as a reliable model for classifying and predicting the bioactivities of the Egyptian EO-containing plants. The overall accuracy for the final prediction process is reported as approximately 97%. Hereby, the proposed deep learning model could be utilized as an efficient model in predicting the bioactivities of, at least Egyptian, EOs-producing plants.

scholarly journals ResCovNet: A Deep Learning-Based Architecture For COVID-19 Detection From Chest CT Scan Images

2020 ◽  
Author(s):  
Ankan Ghosh Dastider ◽  
Mohseu Rashid Subah ◽  
Farhan Sadik ◽  
Tanvir Mahmud ◽  
Shaikh Anowarul Fattah
Keyword(s):  
Deep Learning ◽  
Ct Scan ◽  
Chest Ct ◽  
Chest Ct Scan

scholarly journals Diabetes Prediction Using Enhanced SVM and Deep Neural Network Learning Techniques

2021 ◽  
Vol 16 (4) ◽  
pp. 1-20
Author(s):  
P. Nagaraj ◽  
P. Deepalakshmi
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Learning Model ◽  
Support Vector ◽  
Neural Network Learning ◽  
Learning Techniques ◽  
Diabetes Prediction ◽  
Deep Learning Model

Diabetes, caused by the rise in level of glucose in blood, has many latest devices to identify from blood samples. Diabetes, when unnoticed, may bring many serious diseases like heart attack, kidney disease. In this way, there is a requirement for solid research and learning model’s enhancement in the field of gestational diabetes identification and analysis. SVM is one of the powerful classification models in machine learning, and similarly, Deep Neural Network is powerful under deep learning models. In this work, we applied Enhanced Support Vector Machine and Deep Learning model Deep Neural Network for diabetes prediction and screening. The proposed method uses Deep Neural Network obtaining its input from the output of Enhanced Support Vector Machine, thus having a combined efficacy. The dataset we considered includes 768 patients’ data with eight major features and a target column with result “Positive” or “Negative”. Experiment is done with Python and the outcome of our demonstration shows that the deep Learning model gives more efficiency for diabetes prediction.

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