SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104025 - 104025

Опубликована: Янв. 1, 2025

Язык: Английский

---

Advancing multiple sclerosis diagnosis through an innovative hybrid AI framework incorporating Multi-view ResNet and quantum RIME-inspired metaheuristics

Ain Shams Engineering Journal, Год журнала: 2025, Номер 16(2), С. 103241 - 103241

Опубликована: Янв. 13, 2025

Язык: Английский

---

A multi-stage fusion deep learning framework merging local patterns with attention-driven contextual dependencies for cancer detection

Computers in Biology and Medicine, Год журнала: 2025, Номер 189, С. 109916 - 109916

Опубликована: Март 6, 2025

Язык: Английский

---

A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD)

Bioengineering, Год журнала: 2024, Номер 11(7), С. 711 - 711

Опубликована: Июль 13, 2024

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep and computer vision, fundamentally transforming the analysis retinal images. By utilizing a wide array visual cues extracted from fundus images, sophisticated artificial intelligence models have been developed diagnose various disorders. This paper concentrates on detection Age-Related Macular Degeneration (AMD), significant condition, by offering an exhaustive examination recent learning methodologies. Additionally, it discusses potential obstacles constraints associated with implementing this technology field ophthalmology. Through systematic review, research aims assess efficacy techniques discerning AMD different modalities as they shown promise disorders diagnosis. Organized around prevalent datasets imaging techniques, initially outlines assessment criteria, image preprocessing methodologies, frameworks before conducting thorough investigation diverse approaches for detection. Drawing insights more than 30 selected studies, conclusion underscores current trajectories, major challenges, future prospects diagnosis, providing valuable resource both scholars practitioners domain.

Язык: Английский

---

Brain tumor detection across diverse MR images: An automated triple-module approach integrating reduced fused deep features and machine learning

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103832 - 103832

Опубликована: Дек. 1, 2024

Язык: Английский

---

Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques

Neuroinformatics, Год журнала: 2025, Номер 23(2)

Опубликована: Янв. 16, 2025

Язык: Английский

---

Automated fall risk classification for construction workers using wearable devices, BIM, and optimized hybrid deep learning

Automation in Construction, Год журнала: 2025, Номер 172, С. 106072 - 106072

Опубликована: Фев. 19, 2025

Язык: Английский

---

Securing SDON with hybrid evolutionary intrusion detection system: An ensemble algorithm for feature selection and classification

Optical Fiber Technology, Год журнала: 2025, Номер 93, С. 104206 - 104206

Опубликована: Март 20, 2025

Язык: Английский

---

Integrating pyramid vision transformer and topological data analysis for brain tumor

Frontiers in Computer Science, Год журнала: 2025, Номер 7

Опубликована: Апрель 10, 2025

Introduction Brain tumor (BT) classification is crucial yet challenging due to the complex and varied nature of these tumors. We present a novel approach combining Pyramid Vision Transformer (PVT) with an adaptive deformable attention mechanism Topological Data Analysis (TDA) address complexities BT detection. While PVT have been explored in prior work, we introduce key innovations enhance their performance for medical image analysis. Methods developed that dynamically adjusts receptive fields based on complexity, focusing critical regions MRI scans. The also incorporates sampling rate hierarchical dynamic position embeddings context-aware multi-scale feature extraction. Feature channels are partitioned into specialized groups via offset group improve diversity, strategy further integrates local global contexts yield refined representations. Additionally, applying TDA images extracts meaningful topological patterns, followed by Random Forest classifier final classification. Results method was evaluated Figshare brain dataset. It achieved 99.2% accuracy, 99.35% recall, 98.9% precision, 99.12% F1-score, Matthews correlation coefficient (MCC) 0.98, LogLoss 0.05, average processing time approximately 6 seconds per image. Discussion These results underscore method's ability combine detailed extraction insights, significantly improving accuracy efficiency proposed offers promising tool more reliable rapid diagnosis.

Язык: Английский

---

Enhanced Optimisation of MPLS Network Traffic using a Novel Adjustable Bat Algorithm with Loudness Optimizer

Results in Engineering, Год журнала: 2025, Номер unknown, С. 104774 - 104774

Опубликована: Апрель 1, 2025

Язык: Английский

---