Dynamic learning for imbalanced data in learning chest X-ray and CT images

Iqbal, Saeed; Qureshi, Adnan N.; Li, Jianqiang; Choudhry, Imran Arshad; Mahmood, Tariq

doi:10.1016/j.heliyon.2023.e16807

Cited by 14 publications

(3 citation statements)

References 75 publications

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“…To combat overfitting and regulate the trade-off between margins and training errors, tuning parameters, particularly the penalty factor C, are utilized [37,38]. For our research, we chose the Radial Basis Function (RBF) kernel for classification and regression tasks.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Naseem

Mahmood

et al. 2023

Preprint

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Numerous educational institutions utilize data mining techniques to manage student records, particularly those related to academic achievements , which are essential in improving learning experiences and overall 1 Springer Nature 2021 L A T E X template outcomes. Educational Data Mining (EDM) is a thriving research field that employs data mining and machine learning methods to extract valuable insights from educational databases, primarily focused on predicting students’ academic performance. This study proposes a novel Federated Learning (FL) standard that ensures the confidentiality of the dataset and allows predicting student grades, categorized into four levels: Low, Good, Average, and Drop. To enhance model precision, optimized features are incorporated into the training process. This study evaluates the optimized dataset using five machine learning (ML) algorithms , namely Support Vector Machine (SVM), Decision Tree, Nã A¯ve Bayes, K-Nearest Neighbours, and the proposed Federated Learning Model. The models’ performance is assessed regarding accuracy, precision , recall, and F1 score, followed by a comprehensive comparative analysis. The results reveal that FL and SVM outperform the alternative models, demonstrating superior predictive performance for student grade classification. This study showcases the potential of Federated Learning in effectively utilizing educational data from various institutes while maintaining data privacy, contributing to educational data mining and machine learning advancements for student performance prediction.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Naseem

Mahmood

et al. 2023

Preprint

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“…15 AI-based deep learning approaches outperform and enhance traditional diagnostic methods by enabling automated analysis of medical images with high accuracy and efficiency. 16 Recent studies have explored the application of pre-trained CNN models, [17][18][19] transfer learning, [20][21][22][23][24] and extreme/ensemble learning [25][26][27] techniques to improve the performance of multi-class Lung disease detection from chest X-rays. Transfer learning allows pre-trained models to be fine-tuned on smaller medical image datasets that enable CNN to perform efficient knowledge transfer and faster convergence during training.…”

mentioning

confidence: 99%

Detection of multi‐class lung diseases based on customized neural network

Ali,

Wang,

Shi

2024

Computational Intelligence

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In the medical image processing domain, deep learning methodologies have outstanding performance for disease classification using digital images such as X‐rays, magnetic resonance imaging (MRI), and computerized tomography (CT). However, accurate diagnosis of disease by medical personnel can be challenging in certain cases, such as the complexity of interpretation and non‐availability of expert personnel, difficulty at pixel‐level analysis, etc. Computer‐aided diagnostic (CAD) systems with proper training have shown the potential to enhance diagnostic accuracy and efficiency. With the exponential growth of medical data, CAD systems can analyze and extract valuable information by assisting medical personnel during the disease diagnostic process. To overcome these challenges, this research introduces CX‐RaysNet, a novel deep‐learning framework designed for the automatic identification of various lung disease classes in digital chest X‐ray images. The core novelty of the CX‐RaysNet framework lies in the integration of both convolutional and group convolutional layers, along with the usage of small filter sizes and the incorporation of dropout regularization. This phenomenon helps us optimize the model's ability to distinguish minute features that reveal different lung diseases. Additionally, data augmentation techniques are implemented to augment the training and testing datasets, which enhances the model's robustness and generalizability. The performance evaluation of CX‐RaysNet reveals promising results, with the proposed model achieving a multi‐class classification accuracy of 97.25%. Particularly, this study represents the first attempt to optimize a model specifically for low‐power embedded devices, aiming to improve the accuracy of disease detection while minimizing computational resources.

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“…While electronic human resources (E-HR) can benefit healthcare workers, The utilization of electronic health records (E-HR) offers significant advantages in delivering appropriate patient care and facilitating the secure exchange of medical records, patient medical histories, medication details, and prescriptions. E-health provides a proficient and immediate therapeutic approach for patients [9]. The necessity to unify and include diverse electronic health data from several sectors, including medical research laboratories, hospitals, and health insurance companies, has given rise to the emergence of an electronic health (e-health) concept.…”

mentioning

confidence: 99%

An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

Butt,

Mahmood,

Saba

et al. 2023

IEEE Access

Self Cite

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In today's world, services are improved and advanced in every field of life. Especially in the health sector, information technology (IT) plays a vigorous role in electronic health (e-health). To achieve benefits from e-health, its cloud-based implementation is necessary. With this environment's multiple benefits, privacy and security loopholes exist. As the number of users grows, the Electronic Healthcare System's (EHS) response time becomes slower. This study presented a trust mechanism for access control (AC) known as role-based access control (RBAC) to address this issue. This method observes the user's behavior and assigns roles based on it. The AC module has been implemented using SQL Server, and an administrator develops controls for users with roles and access to multiple EHS modules. To validate the user's trust value, A .net-based framework has been introduced. The framework of e-health proposed in this research ensures that users can protect their data from intruders and other security threats.

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Dynamic learning for imbalanced data in learning chest X-ray and CT images

Cited by 14 publications

References 75 publications

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Analysis of the Factors Influencing the Predictive Learning Performance Using Federated Learning

Detection of multi‐class lung diseases based on customized neural network

An Optimized Role-Based Access Control Using Trust Mechanism in E-Health Cloud Environment

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