The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection
Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-...
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MDPI AG
2024-12-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/1/2 |
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| author | Tarek Berghout |
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| author_sort | Tarek Berghout |
| collection | DOAJ |
| description | Brain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019–2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics. |
| format | Article |
| id | doaj-art-753dc25ff27e46559f45595283d8d598 |
| institution | Kabale University |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-753dc25ff27e46559f45595283d8d5982025-01-24T13:36:13ZengMDPI AGJournal of Imaging2313-433X2024-12-01111210.3390/jimaging11010002The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor DetectionTarek Berghout0Laboratory of Automation and Manufacturing Engineering, Department of Industrial Engineering, Batna 2 University, Batna 05000, AlgeriaBrain tumor detection is crucial in medical research due to high mortality rates and treatment challenges. Early and accurate diagnosis is vital for improving patient outcomes, however, traditional methods, such as manual Magnetic Resonance Imaging (MRI) analysis, are often time-consuming and error-prone. The rise of deep learning has led to advanced models for automated brain tumor feature extraction, segmentation, and classification. Despite these advancements, comprehensive reviews synthesizing recent findings remain scarce. By analyzing over 100 research papers over past half-decade (2019–2024), this review fills that gap, exploring the latest methods and paradigms, summarizing key concepts, challenges, datasets, and offering insights into future directions for brain tumor detection using deep learning. This review also incorporates an analysis of previous reviews and targets three main aspects: feature extraction, segmentation, and classification. The results revealed that research primarily focuses on Convolutional Neural Networks (CNNs) and their variants, with a strong emphasis on transfer learning using pre-trained models. Other methods, such as Generative Adversarial Networks (GANs) and Autoencoders, are used for feature extraction, while Recurrent Neural Networks (RNNs) are employed for time-sequence modeling. Some models integrate with Internet of Things (IoT) frameworks or federated learning for real-time diagnostics and privacy, often paired with optimization algorithms. However, the adoption of eXplainable AI (XAI) remains limited, despite its importance in building trust in medical diagnostics. Finally, this review outlines future opportunities, focusing on image quality, underexplored deep learning techniques, expanding datasets, and exploring deeper learning representations and model behavior such as recurrent expansion to advance medical imaging diagnostics.https://www.mdpi.com/2313-433X/11/1/2artificial intelligencebrain tumorcancerclassificationdeep learningMRI medical images |
| spellingShingle | Tarek Berghout The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection Journal of Imaging artificial intelligence brain tumor cancer classification deep learning MRI medical images |
| title | The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection |
| title_full | The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection |
| title_fullStr | The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection |
| title_full_unstemmed | The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection |
| title_short | The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection |
| title_sort | neural frontier of future medical imaging a review of deep learning for brain tumor detection |
| topic | artificial intelligence brain tumor cancer classification deep learning MRI medical images |
| url | https://www.mdpi.com/2313-433X/11/1/2 |
| work_keys_str_mv | AT tarekberghout theneuralfrontieroffuturemedicalimagingareviewofdeeplearningforbraintumordetection AT tarekberghout neuralfrontieroffuturemedicalimagingareviewofdeeplearningforbraintumordetection |