augmented-multimodal-fusion-for-optimized-brain-tumor-detection

Augmented Multimodal Fusion for Optimized Brain Tumor Detection: Evaluation and Comparative Analysis

Brain tumors represent a significant medical challenge, necessitating accurate and efficient detection methods for timely intervention. This work ...

Prince Sultan university

Imam Mohammad Ibn Saud Islamic University

With technological advancements, remarkable progress has been made with the convergence of health sciences and Artificial Intelligence (AI). Modern health systems are proposed to ease patient diagnostics. However, the challenge is to provide AI-based precautions to patients and doctors for more accurate risk assessment. The proposed healthcare system aims to integrate patients, doctors, laboratories, pharmacies, and administrative personnel use cases and their primary functions onto a single platform. The proposed framework can also process microscopic images, CT scans, X-rays, and MRI to classify malignancy and give doctors a set of AI precautions for patient risk assessment. The proposed framework incorporates various DCNN models for identifying different forms of tumors and fractures in the human body i.e., brain, bones, lungs, kidneys, and skin, and generating precautions with the help of the Fined-Tuned Large Language Model (LLM) i.e., Generative Pretrained Transformer 4 (GPT-4). With enough training data, DCNN can learn highly representative, data-driven, hierarchical image features. The GPT-4 model is selected for generating precautions due to its explanation, reasoning, memory, and accuracy on prior medical assessments and research studies. Classification models are evaluated by classification report (i.e., Recall, Precision, F1 Score, Support, Accuracy, and Macro and Weighted Average) and confusion matrix and have shown robust performance compared to the conventional schemes.

MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging.

The accurate diagnosis of retinal diseases, such as Diabetic Macular Edema (DME) and Age-related Macular Degeneration (AMD), is essential for preventing vision loss. Optical Coherence Tomography (OCT) imaging plays a crucial role in identifying these conditions, especially given the increasing prevalence of AMD. This study introduces a novel Reinforcement-Based Leveraging Transfer Learning (RBLTL) framework, which integrates reinforcement Q-learning with transfer learning using pre-trained models, including InceptionV3, DenseNet201, and InceptionResNetV2. The RBLTL framework dynamically optimizes hyperparameters, improving classification accuracy and generalization while mitigating overfitting. Experimental evaluations demonstrate remarkable performance, achieving testing accuracies of 98.75%, 98.90%, and 99.20% across three scenarios for multiclass OCT image classification. These results highlight the effectiveness of the RBLTL framework in categorizing OCT images for conditions like DME and AMD, establishing it as a reliable and versatile approach for automated medical image classification with significant implications for clinical diagnostics.

Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.

Fake News (FN) dissemination on websites and online platforms influences human behaviours, socio-political domains, and the sovereignty of a country. The outpour of biased news and propaganda on online portals can be addressed by restricting online propaganda using an automated mechanism. Proving the authenticity of news and information on online platforms in regional languages, such as Urdu, with limited resources and datasets, is challenging. Furthermore, limited research in resource-constrained languages has created language bias in Artificial Intelligence (AI) research, which is concentrated in this study. Natural Language Processing (NLP) techniques have been used for Fake News Detection (FND) for English news and various language-related tasks. Previous studies used Machine Learning (ML), Deep Learning (DL), and individual Pre-trained Language Models (PLMs) for Urdu FND. ML-based ensemble model showed better performance than pre-trained models for Urdu FND. We propose a methodology for Urdu FND by applying stacked ensemble learning of PLMs, ELECTRA, mBERT and XLM-RoBERTa after apposite fine-tuning and hyperparameter optimization. To overcome the limitations of each pre-trained transformer model, these are fine-tuned individually using a publicly available Urdu dataset. The prediction performance results of the proposed stacking approach surpass the performance of each pre-trained model. An Accuracy of 0.914, a Matthews Correlation Co-efficient (MCC) value of 0.898, and an F1-score of 0.904 validate the efficacy of the proposed ensemble model.

Multi-domain Urdu fake news detection using pre-trained ensemble model.

Owing to the proliferation of mobile devices, Google's Android operating system has become a dominant force in global communication. However, its popularity makes it a prime target for cyberattacks. Effective malware detection systems are crucial for combating these escalating threats, particularly amid the evolving use of adversarial examples to evade detection. These systems employ static and dynamic analysis methodologies with machine learning, particularly Generative Adversarial Networks (GANs), which play a key role. The Android Opcode Modification GAN enhances malware detection by intelligently modifying opcode distribution features using the Opcode Frequency Optimal Adjustment algorithm. Despite its effectiveness, the dual-opponent generative adversarial network (DOpGAN) introduces a grey-box attack strategy that misclassifies generated examples as benign, significantly evading detection. DOpGAN operates by altering opcode distribution features during the generation and insertion process, making it particularly challenging for detection systems to classify correctly. The adversarial examples generated by DOpGAN highlight the critical need to integrate defensive measures such as adversarial example detection systems into the Android security framework. Beyond evasion, these adversarial examples provide invaluable opportunities for retraining and improving malware detection systems, thereby ensuring their resilience against emerging threats. The findings underscore the broader need for continuous innovation in Android security mechanisms, fostering collaboration between academia and industry to protect users and systems in an ever-evolving mobile security landscape.

GEAAD: generating evasive adversarial attacks against android malware defense.

Fake News (FN) prevalence on Online Social Networks (OSNs) and online websites is a worldwide issue. The previous studies on Fake News Detection (FND) have focused on rich-resource languages with limited relevance to users other than native speakers. Despite the progressing multilingual approaches, FND in low-resource languages remains obscure due to a lack of large-sized annotated corpora of real-world news. Large Language Models (LLMs) have emerged as a promising solution for multilingual FND. This study leverages the power of LLMs for an automated mechanism compared to traditional feature extraction methods. We curate the first large-sized multi-domain corpus, the Hook and Bait Urdu, with 78,409 fake and true news, and fine-tune the LLaMA 2 model for our proposed approach. We implement the curated dataset for two experiments. First, this study evaluates the dataset for unimodal text-based Urdu FND. The proposed LLaMA-based approach shows an accuracy of 0.978 and an F1-score of 0.971. For our second experiment, we fine-tuned the LLaMA 2-based framework for multilingual FND using the curated dataset (in Urdu) and the ISOT Fake News dataset (in English). Analytical and prediction performance comparisons with the previous studies validate the efficacy of the proposed framework with an accuracy of 0.984 and an F1-score of 0.980. The lightweight LoRA fine-tuning method, with  0.032% trainable parameters, ensured robust data handling, computational efficiency while leveraging early stopping and optimized hyperparameters for reliable and high-performing monolingual and multilingual FND. The real-world news dataset is publicly available for developing an automated FND mechanism to curb the threat of FN and related cybercrimes.

Article	Year
MediScan: A Framework of U-Health and Prognostic AI Assessment on Medical Imaging. Journal of imaging\| PubMed ID: 39728219	2024
Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification. Scientific reports\| PubMed ID: 39979354	2025
Multi-domain Urdu fake news detection using pre-trained ensemble model. Scientific reports\| PubMed ID: 40082485	2025
GEAAD: generating evasive adversarial attacks against android malware defense. Scientific reports\| PubMed ID: 40195378	2025
Benchmarking Hook and Bait Urdu news dataset for domain-agnostic and multilingual fake news detection using large language models. Scientific reports\| PubMed ID: 40319160	2025

Mohammed Alshara

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