An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images

Awotunde, Joseph Bamidele; Ajagbe, Sunday Adeola; Oladipupo, Matthew A.; Awokola, J. A.; Afolabi, Olakunle Sunday; Mathew, Timothy O.; Oguns, Yetunde J.

doi:10.1007/978-3-030-89654-6_23

Cited by 26 publications

(6 citation statements)

References 26 publications

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“…This was another landmark achievement of DL-based application, it gave further confidence in to TLD, an innovation that aids smart city development. Also, high-performance face identification, according to [17], [18] was a difficult problem to solve, especially when there were many little faces. The researchers found that the current face detection algorithms prioritize a high recall rate while neglecting the problem of too many false positives.…”

Section: Related Studiesmentioning

confidence: 99%

Performance investigation of two-stage detection techniques using traffic light detection dataset

Ajagbe

Adegun

Olanrewaju

et al. 2023

IJ-AI

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<span>Using a camera to monitor an object or a group of objects over time is the process of object detection. It can be used for a variety of things, including security and surveillance, video communication, traffic light detection (TLD), object detection from compressed video in public places. In recent times, object tracking has become a popular topic in computer science particularly, the data science community, thanks to the usage of deep learning (DL) in artificial intelligence (AI). DL which convolutional neural network (CNN) as one of its techniques usually used two-stage detection methods in TLD. Despite all successes recorded in TLD through the use of two-stage detection methods, there is no study that has analyzed these methods in experimental research, studying the strength and witnesses by the researchers. Based on the needs this study analyses the applications of DL techniques in TLD. We implemented object detection for TLD using 5 two-stage detection methods with the traffic light dataset using a Jupyter notebook and the sklearn libraries. We present the achievements of two-stage detection methods in TLD, going by standard performance metrics used, FASTER-CNN was the best in detection accuracy, F1-score, precision and recall with 0.89, 0.93, 0.83 and 0.90 respectively.</span>

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Section: Related Studiesmentioning

confidence: 99%

Performance investigation of two-stage detection techniques using traffic light detection dataset

Ajagbe

Adegun

Olanrewaju

et al. 2023

IJ-AI

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“…Images of Alzheimer's disease were divided into four categories by neurologists, and the results were assessed using a range of metrics, where VGG-19 was the best in three categories. The research in [33] proposed a machine-learning diagnostic system for COVID-19. For a quicker and more accurate detection of possible COVID-19 instances, four machine learning algorithms-Random Forest (RF), XGBoost, and Light Gradient Boosting Machine (LGBM)-were applied.…”

Section: Related Workmentioning

confidence: 99%

Multimodal Stereotactic Brain Tumor Segmentation Using 3D-Znet

et al. 2023

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Stereotactic brain tumor segmentation based on 3D neuroimaging data is a challenging task due to the complexity of the brain architecture, extreme heterogeneity of tumor malformations, and the extreme variability of intensity signal and noise distributions. Early tumor diagnosis can help medical professionals to select optimal medical treatment plans that can potentially save lives. Artificial intelligence (AI) has previously been used for automated tumor diagnostics and segmentation models. However, the model development, validation, and reproducibility processes are challenging. Often, cumulative efforts are required to produce a fully automated and reliable computer-aided diagnostic system for tumor segmentation. This study proposes an enhanced deep neural network approach, the 3D-Znet model, based on the variational autoencoder–autodecoder Znet method, for segmenting 3D MR (magnetic resonance) volumes. The 3D-Znet artificial neural network architecture relies on fully dense connections to enable the reuse of features on multiple levels to improve model performance. It consists of four encoders and four decoders along with the initial input and the final output blocks. Encoder–decoder blocks in the network include double convolutional 3D layers, 3D batch normalization, and an activation function. These are followed by size normalization between inputs and outputs and network concatenation across the encoding and decoding branches. The proposed deep convolutional neural network model was trained and validated using a multimodal stereotactic neuroimaging dataset (BraTS2020) that includes multimodal tumor masks. Evaluation of the pretrained model resulted in the following dice coefficient scores: Whole Tumor (WT) = 0.91, Tumor Core (TC) = 0.85, and Enhanced Tumor (ET) = 0.86. The performance of the proposed 3D-Znet method is comparable to other state-of-the-art methods. Our protocol demonstrates the importance of data augmentation to avoid overfitting and enhance model performance.

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“…Organizational health behavior is a complex concept with many facets, including social, psychological, and physical components. Research can obtain a more complete knowledge of corporate health behavior by using both qualitative and quantitative metrics [47]. The viewpoints, experiences, and interpretations of participants can be thoroughly explored using qualitative methods.…”

Section: Subscale B (Qualitative Scale)mentioning

confidence: 99%

Organizational Health Behavior Index (OHBI): A Tool for Measuring Organizational Health

Jaafari,

Alzuman,

Ali

et al. 2023

Sustainability

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Organizational health helps companies achieve their goals, gain a competitive edge, and boost employee engagement and well-being by determining a company’s ability to establish, communicate, and implement a unified business strategy while retaining highly engaged employees. The current study aimed to develop and validate an original two-subscale measure that evaluates organizational health behavior determinants, including a quantitative 5-point Likert-type “Subscale A” of 16 items in 5 dimensions (awareness, appreciation, relations, employee engagement, and internal communication) and a qualitative “Subscale B” of 10 items in 3 dimensions (employee persona, organizational culture, and employee voice) called the Organization Health Behavior Index (OHBI). The combination of the two methodologies enhances the study’s credibility, validity, and applicability, as well as its contribution to the field of organizational health behavior research. Furthermore, the study included three sample groups and a sample of 3510 respondents from five sectors in the Kingdom of Saudi Arabia: agriculture, education, government, health, and logistics. This research includes various stages in the development and evaluation of the psychometric properties of the OHBI. These stages include conducting reliability analysis, performing exploratory and confirmatory factor analyses, and finalizing the scale. The model’s fit indices, such as the chi-squared test, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR), collectively demonstrated a favorable fit for the model. The findings presented in this study offer support for the reliability and validity of the OHBI scale. The OHBI scale comprehensively encompasses the necessary psychometric properties related to its construction and validation, thereby establishing its validity and reliability as a robust measure of organizational health behavior. The OHBI can help create an environment at work that boosts productivity, engagement, and retention. Organizations can compare their health behavior ratings to industry standards or best practices using the index.

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An Improved Machine Learnings Diagnosis Technique for COVID-19 Pandemic Using Chest X-ray Images

Cited by 26 publications

References 26 publications

Performance investigation of two-stage detection techniques using traffic light detection dataset

Performance investigation of two-stage detection techniques using traffic light detection dataset

Multimodal Stereotactic Brain Tumor Segmentation Using 3D-Znet

Organizational Health Behavior Index (OHBI): A Tool for Measuring Organizational Health

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