Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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COVID-19 Diagnosis from Chest X-ray Images Using Deep Learning Approach

2020 International Conference on Advanced Science and Engineering (ICOASE) ◽

10.1109/icoase51841.2020.9436614 ◽

2020 ◽

Author(s):

Noor N. Qaqos ◽

Omar S. Kareem

Keyword(s):

Deep Learning ◽

Learning Approach ◽

X Ray ◽

Chest X Ray

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Smart access development for classifying lung disease with chest x-ray images using deep learning

Materials Today Proceedings ◽

10.1016/j.matpr.2021.03.650 ◽

2021 ◽

Author(s):

Tarunika kumaraguru ◽

P. Abirami ◽

K.M. Darshan ◽

S.P. Angeline Kirubha ◽

S. Latha ◽

...

Keyword(s):

Deep Learning ◽

Lung Disease ◽

X Ray ◽

Chest X Ray

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Accurate Detection of COVID-19 Using K-EfficientNet Deep Learning Image Classifier and K-COVID Chest X-Ray Images Dataset

2020 IEEE 6th International Conference on Computer and Communications (ICCC) ◽

10.1109/iccc51575.2020.9344949 ◽

2020 ◽

Author(s):

Papa Abdou Karim Karou Diallo ◽

Yun Ju

Keyword(s):

Deep Learning ◽

X Ray ◽

Accurate Detection ◽

Chest X Ray

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Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

European Radiology Experimental ◽

10.1186/s41747-020-00203-z ◽

2021 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Isabella Castiglioni ◽

Davide Ippolito ◽

Matteo Interlenghi ◽

Caterina Beatrice Monti ◽

Christian Salvatore ◽

...

Keyword(s):

Deep Learning ◽

Area Under The Curve ◽

Clinical Information ◽

Training Dataset ◽

Independent Dataset ◽

X Ray ◽

Learning Classifier ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

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