The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection DOI Creative Commons
Tarek Berghout

Journal of Imaging, Год журнала: 2024, Номер 11(1), С. 2 - 2

Опубликована: Дек. 24, 2024

Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early accurate diagnosis vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming error-prone. The rise of deep learning has led advanced models automated brain feature extraction, segmentation, classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 papers past half-decade (2019-2024), this review fills that gap, exploring the latest methods paradigms, summarizing key concepts, challenges, datasets, offering insights into future directions using learning. This also incorporates an analysis previous targets three main aspects: results revealed primarily focuses on Convolutional Neural Networks (CNNs) their variants, with a strong emphasis transfer pre-trained models. Other Generative Adversarial (GANs) Autoencoders, used while Recurrent (RNNs) employed time-sequence modeling. Some integrate Internet Things (IoT) frameworks or federated real-time diagnostics privacy, paired optimization algorithms. However, adoption eXplainable AI (XAI) remains limited, despite its importance building trust diagnostics. Finally, outlines opportunities, focusing image quality, underexplored techniques, expanding deeper representations model behavior recurrent expansion advance imaging

Язык: Английский

Early Brain Tumour Cell Detection With High‐Sensitivity Terahertz Sensors Based on Photonic Crystal Fibre DOI Creative Commons

Diponkar Kundu,

Md. Sohel Rana,

M. Naim

и другие.

IET Nanodielectrics, Год журнала: 2025, Номер 8(1)

Опубликована: Янв. 1, 2025

ABSTRACT Early detection of brain tumours is crucial for timely treatment, improving survival rates, and preventing severe neurological complications. When successful procedures early identification are applied to tumours, it might preserve people. The article illustrates an original biological device finding the first signs tumour cells based on photonic crystal fibre (PCF) equipment performing within terahertz (THz) band. suggested scanner a helpful instrument in tissue diagnosis due its extremely sensitive nature along with minimal transmission degradation. fibre's distinctive arrangement, utilised by frequency spectrum, permits accurate classification healthy parts according differences electromagnetic features. Typical contain damage, as well that cancerous. juxtaposed previous PCF‐based indicators, recommended has excellent comparative response expenses. sensing relative sensitivity 99.26%, effective area 4.77 × 10 −8 m 2 , confinement loss 9.55 −6 cm −1 low material 0.00219 . findings this investigation indicate big step forward observing equipment, providing hopeful approach prompt possible commercial diagnostic possibilities.

Язык: Английский

Процитировано

1
Enhancing Transparency and Trust in Brain Tumor Diagnosis: An In-Depth Analysis of Deep Learning and Explainable AI Techniques DOI Creative Commons
Krishan Kumar,

Kiran Jyoti

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 30, 2025

Abstract Brain tumors pose significant health risks due to their high mortality rates and challenges in early diagnosis. Advances medical imaging, particularly MRI, combined with artificial intelligence (AI), have revolutionized tumor detection, segmentation, classification. Despite the accuracy of models such as Convolutional Neural Networks (CNNs) Vision Transformers (ViTs), clinical adoption is hampered by a lack interpretability. This study provides comprehensive analysis machine learning, deep explainable AI (XAI) techniques brain diagnosis, emphasizing strengths, limitations, potential improve transparency trust. By reviewing 53 peer-reviewed articles published between 2017 2024, we assess current state research, identify gaps, provide practical recommendations for clinicians, regulators, developers. The findings reveal that while XAI techniques, Grad-CAM, SHAP, LIME, significantly enhance model interpretability, remain terms generalizability, computational complexity, dataset quality. Future research should focus on addressing these limitations fully realize diagnostics.

Язык: Английский

Процитировано

0
The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection DOI Creative Commons
Tarek Berghout

Journal of Imaging, Год журнала: 2024, Номер 11(1), С. 2 - 2

Опубликована: Дек. 24, 2024

Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early accurate diagnosis vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming error-prone. The rise of deep learning has led advanced models automated brain feature extraction, segmentation, classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 papers past half-decade (2019-2024), this review fills that gap, exploring the latest methods paradigms, summarizing key concepts, challenges, datasets, offering insights into future directions using learning. This also incorporates an analysis previous targets three main aspects: results revealed primarily focuses on Convolutional Neural Networks (CNNs) their variants, with a strong emphasis transfer pre-trained models. Other Generative Adversarial (GANs) Autoencoders, used while Recurrent (RNNs) employed time-sequence modeling. Some integrate Internet Things (IoT) frameworks or federated real-time diagnostics privacy, paired optimization algorithms. However, adoption eXplainable AI (XAI) remains limited, despite its importance building trust diagnostics. Finally, outlines opportunities, focusing image quality, underexplored techniques, expanding deeper representations model behavior recurrent expansion advance imaging

Язык: Английский

Процитировано

2