Rough Set Based Feature Selection for Egyptian Neonatal Jaundice

Azar

et al. 2016

Neural Comput & Applic

Self Cite

Medical data classification is applied in intelligent medical decision support system to classify diseases into different categories. Several classification methods are commonly used in various healthcare settings. These techniques are fit for enhancing the nature of prediction, initial identification of sicknesses and disease classification. The categorization complexities in healthcare area are focused around the consequence of healthcare data investigation or depiction of medicine by the healthcare professions. This study concentrates on applying uncertainty (i.e. rough set)-based pattern classification techniques for UCI healthcare data for the diagnosis of diseases from different patients. In this study, covering-based rough set classification (i.e. proposed pattern classification approach) is applied for UCI healthcare data. Proposed CRS gives effective results than delicate pattern classifier model. The results of applying the CRS classification method to UCI healthcare data analysis are based upon a variety of disease diagnoses. The execution of the proposed covering-based rough set classification is contrasted with other approaches, such as rough set (RS)-based classification methods, Kth nearest neighbour, improved bijective soft set, support vector machine, modified soft rough set and back propagation neural network methodologies using different evaluating measures.

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Covering-based rough set classification system

Kumar

Azar

et al. 2016

Neural Comput & Applic

Self Cite

“…Deep learning technology, which evolved from Artificial Neural Networks (ANN), has become a major issue in the computer world and is widely used in fields such as healthcare, image identification, text analytics, cybersecurity, and many more (Dudekula et al, 2023;Fati et al, 2022;Boulmaiz et al, 2022, Zaidi et al 2022Ganesan et al, 2022;Abbas et al, 2022;Azar et al, 2021a,b;Ibrahim et al, 2020;Ramadan et al, 2022;Aslam et al, 2021). Machine Learning (ML) is an artificial intelligence subset that generates dynamic algorithms capable of making data-driven judgments (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Inbarani et al, 2022Inbarani et al, , 2020Inbarani et al, , 2018Inbarani et al, , 2015aInbarani et al, ,b, 2014aFekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;Sundaram et al, 2021;Hussien et al, 2020;Mjahed et al, 2020 ;Sayed et al, 2019;Aboamer et al, 2019Aboamer et al, , 2014aSallam et al, 2020;Kumar et al, 2017Banu et al, 2017Banu et al, , 2014Ben Abdallah et al, 2016Fredj et al, 2016 ;Malek and Azar, 2016a,b;Malek et al, 2015a,b;…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Adaptive Histogram Binning-Based Lesion Segmentation for Discerning Severity in COVID-19 Chest CT Scan Images

Nivetha

International Journal of Sociotechnology and Knowledge Development

2023

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Coronavirus sickness (COVID-19) recently adversely disrupted the medical care system and the entire economy. Doctors, researchers, and specialists are working on new-fangled methods to detect COVID-19 relatively efficiently, such as constructing computerized COVID-19 detection systems. Medical imaging, such as Computed Tomography (CT), has a lot of opportunity as a solution to RT-PCR approaches for quantitative assessment and disease monitoring. COVID-19 diagnosis based on CT images can provide speedy and accurate results. A quantitative criterion for diagnosis is provided by an automated segmentation method of infection areas in the lungs. As an outcome, automatic image segmentation is in high demand as a clinical decision aid tool. To detect COVID-19, Computed Tomography images might be employed instead of the time-consuming RT-PCR assay. In this research, a unique technique is provided for segmenting infection areas in the lungs using CT scan images from COVID-19 patients. “Ground Glass Opacity (GGO)” regions were detected using Novel Adaptive Histogram Binning Based Lesion Segmentation (NAHBLS) method. Many metrics were also employed to evaluate the proposed method, including “Sorensen–Dice similarity”, “Sensitivity”, “Specificity”, “Precision”, and “Accuracy” measures. Experiments have shown that the proposed method can effectively separate the lung infections with good accuracy. The results show that the proposed Novel Adaptive Histogram Binning Based Lesion Segmentation based on automatic approach is effective at segmenting the lesion region of the image and calculated the Infection Rate (IR) over the lung region in Computed Tomography scan.

“…Machine Learning (ML) is a subset of artificial intelligence that creates dynamic algorithms capable of generating data-driven decisions (Hussain et al, 2023;Atteia et al, 2023;Salam et al, 2021Salam et al, , 2022Mathiyazhagan et al, 2022;Ashfaq et al, 2022a,b;Fekik et al, 2021Fekik et al, , 2018aEl Kafazi et al, 2021;Bouakrif et al, 2019;Sundaram et al, 2021;Hussien et al, 2020;Mjahed et al, 2020 ;Sayed et al, 2019;Aboamer et al, 2019Aboamer et al, , 2014aSallam et al, 2020;Kumar et al, 2017Banu et al, 2017Banu et al, , 2014Ben Abdallah et al, 2016Fredj et al, 2016 ;Malek and Azar, 2016a,b;Vaidyanathan & Azar, 2016;Zhu & Azar, 2015;Malek et al, 2015a,b;Ding et al, 2015;Elshazly et al, 2013a,b,c).…”

Section: Deep Trs Architecture (Dtrsa)mentioning

confidence: 99%

Novel Architecture for Image Classification Based on Rough Set

Nivetha

International Journal of Service Science, Management, Engineering, and Technology

2023

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The Computed Tomography (CT) scan images classification problem is one of the most challenging problems in recent years. Different medical treatments have been developed based on the correctness of CT scan images classification. In this work, a novel deep learning architecture is proposed to correctly diagnose COVID-19 patients using CT scan images. In fact, a new classifier based on rough set theory is suggested. Extensive experiments showed that the novel deep learning architecture provides a significant improvement over well-known classifier. The new classifier produces 95% efficiency and a very low error rate on different metrics. The suggested deep learning architecture coupled with novel tolerance outperforms the other standard classification approaches for the detection of COVID-19 using CT-Scan images.