A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19)

Ahmadian, Sajad; Jalali, Seyed Mohammad Jafar; Islam, Syed; Khosravi, Abbas; Fazli, Ebrahim; Nahavandi, Saeid

doi:10.1016/j.compbiomed.2021.104994

Cited by 24 publications

(12 citation statements)

References 76 publications

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“…Moreover Ahmadian et al [ 66 ] developed a novel two-phase improved deep neuroevolution model to COVID-19 diagnosis from chest X-ray data. The deep neuroevolution algorithm developed in this paper is tested on a real-world dataset, and its performance was indicated by comparing different evaluation metrics.…”

Section: Introductionmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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Section: Introductionmentioning

confidence: 99%

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Rostami

Oussalah

2022

Informatics in Medicine Unlocked

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“…CNNs, along with transformers, currently dominate deep learning applications in radiology. DNE computer vision research has focused mostly on hyperparameter tuning, both outside [26,27,28] and inside of radiology [29,30,31]. DNE can also optimize neural network parameters, forming an alternative to gradient descent.…”

Section: Rano and Recist Are The Prevalent Formal Methods To Assess T...mentioning

confidence: 99%

Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution

Stember¹,

Young²,

Shalu³

2022

Preprint

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Purpose A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example as per RE-CIST or RANO criteria, is tedious, time-consuming, and can miss important tumor response information; most notably, such criteria often exclude lesions, the non-target lesions, altogether. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Because genetic sub-types of cancer can be fairly specific and patient enrollment in therapy trials is often limited in number and accrual rate, we wish to make response assessments with small training sets. deep neuroevolution (DNE) can produce radiology artificial intelligence (AI) that performs well on small training sets. Following recent work in which we used DNE to train the parameters of a small convolutional neural network (CNN) for MRI sequence identification, we have now used a DNE parameter search to optimize a CNN that predicts progression versus regression of metastatic brain disease.Methods We analyzed 50 pairs of MRI contrast-enhanced images as our training set. Half of these pairs, separated in time, qualified as disease progression, while the other 25 images constituted regression. We trained the parameters of a relatively small CNN via "mutations" that consisted of random CNN weight adjustments and mutation "fitness." We then incorporated the best mutations into the next generation's CNN, repeating

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“…In the modern era, more and more healthcare data are being collected by computer systems and storage technologies [167]. Massive data growth in healthcare fields has posed a major challenge to healthcare data analysis techniques [168][169][170]. Medical doctors and specialists cannot analyze such an overwhelming amount of medical data in a short period of time to make medical diagnoses, predictions, or treatment schedules.…”

Section: A Healthcare Event Monitoring and Disease Diagnosismentioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

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A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19)

Cited by 24 publications

References 76 publications

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest

Direct evaluation of progression or regression of disease burden in brain metastatic disease with Deep Neuroevolution

Community Detection Algorithms in Healthcare Applications: A Systematic Review

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