A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of COVID 19 Through X Rays Images

Rabbah, Jalal; Ridouani, Mohammed; Hassouni, Larbi

doi:10.1109/icds50568.2020.9268777

Cited by 22 publications

(5 citation statements)

References 11 publications

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“…To mitigate this classification problem, a stacking model-based ensemble-learning technique is used to achieve more stable and accurate predictions than with individual learning. Stacking or Stacked Generalization is an advanced technique that aims at combining the prediction results of multiple individual learning models to take the final predictions [29]. The stacking architecture includes two or more base models, commonly denoted by level-0 models, and a meta-model denoted by a level-1 model [30], as shown in Figure 3.…”

Section: Classification Modelingmentioning

confidence: 99%

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Benazzouza

Ridouani²,

Salahdine³

et al. 2022

Sensors

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Cooperative network is a promising concept for achieving a high-accuracy decision of spectrum sensing in cognitive radio networks. It enables a collaborative exchange of the sensing measurements among the network users to monitor the primary spectrum occupancy. However, the presence of malicious users leads to harmful interferences in the system by transmitting incorrect local sensing observations.To overcome this security related problem and to improve the accuracy decision of spectrum sensing in cooperative cognitive radio networks, we proposed a new approach based on two machine learning solutions. For the first solution, a new stacking model-based malicious users detection is proposed, using two innovative techniques, including chaotic compressive sensing technique-based authentication for feature extraction with a minimum of measurements and an ensemble machine learning technique for users classification. For the second solution, a novel deep learning technique is proposed, using scalogram images as inputs for the primary user spectrum’s classification. The simulation results show the high efficiency of both proposed solutions, where the accuracy of the new stacking model reaches 97% in the presence of 50% of malicious users, while the new scalogram technique-based spectrum sensing is fast and achieves a high probability of detection with a lower number of epochs and a low probability of false alarm.

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Section: Classification Modelingmentioning

confidence: 99%

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Benazzouza

Ridouani²,

Salahdine³

et al. 2022

Sensors

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“…The study proposed by Rabbah et al (2020) uses an automated method for detecting COVID-19. It takes CT Scan images as input.…”

Section: Number Of Images Predictionmentioning

confidence: 99%

Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey

Kumar¹,

Atalla

Almuraqab

et al. 2022

Front. Artif. Intell.

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Graphical-design-based symptomatic techniques in pandemics perform a quintessential purpose in screening hit causes that comparatively render better outcomes amongst the principal radioscopy mechanisms in recognizing and diagnosing COVID-19 cases. The deep learning paradigm has been applied vastly to investigate radiographic images such as Chest X-Rays (CXR) and CT scan images. These radiographic images are rich in information such as patterns and clusters like structures, which are evident in conformance and detection of COVID-19 like pandemics. This paper aims to comprehensively study and analyze detection methodology based on Deep learning techniques for COVID-19 diagnosis. Deep learning technology is a good, practical, and affordable modality that can be deemed a reliable technique for adequately diagnosing the COVID-19 virus. Furthermore, the research determines the potential to enhance image character through artificial intelligence and distinguishes the most inexpensive and most trustworthy imaging method to anticipate dreadful viruses. This paper further discusses the cost-effectiveness of the surveyed methods for detecting COVID-19, in contrast with the other methods. Several finance-related aspects of COVID-19 detection effectiveness of different methods used for COVID-19 detection have been discussed. Overall, this study presents an overview of COVID-19 detection using deep learning methods and their cost-effectiveness and financial implications from the perspective of insurance claim settlement.

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“…The health care system has benefited from the advanced development of medical artificial intelligence. This technology plays an important role in the decision-making of clinicians in the formulation of a diagnosis, the making of therapeutic decisions, and the prediction of treatment results [36,37,38,39].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning in Healthcare: A Survey on Brain Tumor Detection

Elaissaoui

Ridouani

2023

ITM Web Conf.

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Brain tumors are one of the most dangerous diseases that continue to be threatened worldwide.As a result, the diagnosis and treatment in the early stages are very important in this case. As a result, the diagnosis and treatment in the early stages are very important in this case. Furthermore, the determination of the correct nature of the tumor is a sensitive process in patient treatment .In recent years, with the advancement of deep learning solutions in computer vision, such as image segmentation, image classification, and object detection, promising results have been achieved in the accuracy of medical diagnosis.In this paper, we propose the most famous deep learning model and architecture used to predict the existence of brain tumors from an MR image dataset.

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A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of COVID 19 Through X Rays Images

Cited by 22 publications

References 11 publications

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

Detection of COVID-19 Using Deep Learning Techniques and Cost Effectiveness Evaluation: A Survey

Application of Deep Learning in Healthcare: A Survey on Brain Tumor Detection

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