a-novel-approach-using-ai-vision-transformer-for-ct-scan-analysis-for

A Novel Approach Using AI-vision Transformer for CT Scan Analysis for Lung Cancer Detection

Diagnosing lung cancer remains challenging because of the subtle differences between benign versus malignant disease on imaging studies. While ...

Najran University

Early detection of abnormalities in chest X-rays is essential for COVID-19 diagnosis and analysis. It can be effective for controlling pandemic spread by contact tracing, as well as for effective treatment of COVID-19 infection. In the proposed work, we presented a deep hybrid learning-based framework for the detection of COVID-19 using chest X-ray images. We developed a novel computationally light and optimized deep Convolutional Neural Networks (CNNs) based framework for chest X-ray analysis. We proposed a new COV-Net to learn COVID-specific patterns from chest X-rays and employed several machine learning classifiers to enhance the discrimination power of the presented framework. Systematic exploitation of max-pooling operations facilitates the proposed COV-Net in learning the boundaries of infected patterns in chest X-rays and helps for multi-class classification of two diverse infection types along with normal images. The proposed framework has been evaluated on a publicly available benchmark dataset containing X-ray images of coronavirus-infected, pneumonia-infected, and normal patients. The empirical performance of the proposed method with developed COV-Net and support vector machine is compared with the state-of-the-art deep models which show that the proposed deep hybrid learning-based method achieves 96.69% recall, 96.72% precision, 96.73% accuracy, and 96.71% F-score. For multi-class classification and binary classification of COVID-19 and pneumonia, the proposed model achieved 99.21% recall, 99.22% precision, 99.21% F-score, and 99.23% accuracy.

Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images.

The Internet of vehicles (IoVs) is an innovative paradigm which ensures a safe journey by communicating with other vehicles. It involves a basic safety message (BSM) that contains sensitive information in a plain text that can be subverted by an adversary. To reduce such attacks, a pool of pseudonyms is allotted which are changed regularly in different zones or contexts. In base schemes, the BSM is sent to neighbors just by considering their speed. However, this parameter is not enough because network topology is very dynamic and vehicles can change their route at any time. This problem increases pseudonym consumption which ultimately increases communication overhead, increases traceability and has high BSM loss. This paper presents an efficient pseudonym consumption protocol (EPCP) which considers the vehicles in the same direction, and similar estimated location. The BSM is shared only to these relevant vehicles. The performance of the purposed scheme in contrast to base schemes is validated via extensive simulations. The results prove that the proposed EPCP technique outperformed compared to its counterparts in terms of pseudonym consumption, BSM loss rate and achieved traceability.

Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles.

A brain tumor is a collection of abnormal cells in the brain that can become life-threatening due to its ability to spread. Therefore, a prompt and meticulous classification of the brain tumor is an essential element in healthcare care. Magnetic Resonance Imaging (MRI) is the central resource for producing high-quality images of soft tissue and is considered the principal technology for diagnosing brain tumors. Recently, computer vision techniques such as deep learning (DL) have played an important role in the classification of brain tumors, most of which use traditional centralized classification models, which face significant challenges due to the insufficient availability of diverse and representative datasets and exacerbate the difficulties in obtaining a transparent model. This study proposes a collaborative federated learning model (CFLM) with explainable artificial intelligence (XAI) to mitigate existing problems using state-of-the-art methods.

Explainable AI in medical imaging: an interpretable and collaborative federated learning model for brain tumor classification.

Brain tumor categorization on MRI is a challenging but crucial task in medical imaging, requiring high resilience and accuracy for effective diagnostic applications. This study describe a unique multimodal scheme combining the capabilities of deep learning with ensemble learning approaches to overcome these issues.

Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM.

Precise classification of lung cancer stages based on CT images remains a significant challenge in oncology. This is vitally necessary for determining prognosis and creating practical treatment plans. Traditional methods mainly rely on human interpretation, which can be inconsistent and prone to fluctuation. To overcome these limitations an automated deep learning model based on the EfficientNet-B0 based architecture is proposed. Explainable AI features enhanced through Gradient-weighted Class Activation Mapping (Grad-CAM) help further boost this model. Training of the model was conducted with 1,190 CT scans from the IQ-OTH/NCCD dataset. All the images fell into the benign, malignant, and normal categories. The suggested technique performs remarkably well, reaching 99% accuracy, 99% precision, and recall rates of 96% for benign cases, 99% for malignant cases, and 100% for normal occurrences. Grad-CAM makes the model more interpretable and transparent by providing visual explanations of its results. It identifies the most important regions in the scans that significantly contribute to the classification results. Apart from contributing to the field of medical image analysis, accurate precision and complete explanations also bring automated diagnosis systems credibility and reliability.

Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping.

The increasing volumes and sophistication of cyber threats, particularly Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks, pose significant dangers to contemporary network structures, particularly the Internet of Things (IoT) environment. Conventional Intrusion Detection Systems (IDS) are also becoming obsolete because they perform detection in a built-in manner and are unable to capture the time trends of dynamic changes of threats. To eliminate such shortcomings, a new hybrid deep learning architecture named the Neural Turing Machine-Gated Recurrent Unit (NTM-GRU) model is proposed in this paper that incorporates the external memory of NTMs and extra temporal learning power of GRUs. The architecture supports analysis on dual timescales, which in turn captures short- and long-term dependencies, exposing the model to unravel complex, low, slow, and zero-day intrusions with recall. Huge testing on the standard sets (UNSW-NB15 and BoT-IoT) and actual (CICIDS2017 and CSE-CID-IS2018 ) demonstrate the high effectiveness of the usage of the model, reaching an accuracy of 99.98%, F1-scores of up to 96% on unknown threats, and the low false positive rates (less than 0.4%). The proposed framework can be applied in both industrial settings and high-speed network settings, where the real-time inference speed was measured at 2.3 milliseconds. The model also incorporates interpretability aspects, making it suitable for Security Operation Centres (SOCs). This work, through the merger of complex memory neural-network structures with cybersecurity needs and requirements encountered in the world, can be realized as providing a scalable, adaptive, and interpretable intrusion detection module, establishing a new state-of-the-art standard for securing next-generation networks.

Deep memory for deep threats: A novel architecture combining GRUs and deep learning models for IDS.

[This corrects the article DOI: 10.3389/fmed.2025.1625183.].

Correction: Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping.

Article	Year
Computer Aided COVID-19 Diagnosis in Pandemic Era Using CNN in Chest X-ray Images. Life (Basel, Switzerland)\| PubMed ID: 36362864	2022
Anonymity Assurance Using Efficient Pseudonym Consumption in Internet of Vehicles. Sensors (Basel, Switzerland)\| PubMed ID: 37299944	2023
Explainable AI in medical imaging: an interpretable and collaborative federated learning model for brain tumor classification. Frontiers in oncology\| PubMed ID: 40083877	2025
Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM. Frontiers in medicine\| PubMed ID: 40276738	2025
Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping. Frontiers in medicine\| PubMed ID: 41001382	2025
Deep memory for deep threats: A novel architecture combining GRUs and deep learning models for IDS. PloS one\| PubMed ID: 41091807	2025
Correction: Automated classification and explainable AI analysis of lung cancer stages using EfficientNet and gradient-weighted class activation mapping. Frontiers in medicine\| PubMed ID: 41169415	2025

Abdulmajeed Alqhatani

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