Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME

Article Biochemical Research Methods

Deep Learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) With CT Images

Ying Song et al.

Summary: The emergence of COVID-19 has led to the urgent need for accurate diagnoses through CT scans. Researchers have developed a deep learning-based CT diagnosis system to accurately identify COVID-19 patients, achieving high levels of accuracy and reliability.

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS (2021)

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Article Radiology, Nuclear Medicine & Medical Imaging

A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19)

Shuai Wang et al.

Summary: This study utilized artificial intelligence to extract radiological features from CT images of COVID-19, achieving timely and accurate diagnosis of the disease, demonstrating the feasibility of using AI for disease control.

EUROPEAN RADIOLOGY (2021)

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Article Automation & Control Systems

Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence

Mario Versaci et al.

Summary: A new fuzzy edge detector based on fuzzy divergence and fuzzy entropy minimization for the thresholding sub-step in gray-scale images is proposed in this work. The results, evaluated by suitable metrics, are considered to be encouraging when compared qualitatively and quantitatively with those obtained by some well-known I- and II-order edge detectors.

INTERNATIONAL JOURNAL OF FUZZY SYSTEMS (2021)

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Article Computer Science, Information Systems

Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence

Md Manjurul Ahsan et al.

Summary: The study proposes and tests six modified deep learning models to detect SARS-CoV-2 infection from chest X-ray images, with VGG16 and MobileNetV2 showing promising results in accurately identifying patients with COVID-19 symptoms on both datasets. The poorly performing models in Study One significantly improved their accuracy when trained with the larger dataset in Study Two, while models like InceptionResNetV2 and VGG19 demonstrated high accuracy on both datasets, providing an accessible alternative to identify patients with COVID-19.

IEEE ACCESS (2021)

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Editorial Material Pathology