Deep Convolutional Neural Networks in Medical Image Analysis: A Review

Information, Год журнала: 2025, Номер 16(3), С. 195 - 195

Опубликована: Март 3, 2025

Deep convolutional neural networks (CNNs) have revolutionized medical image analysis by enabling the automated learning of hierarchical features from complex imaging datasets. This review provides a focused CNN evolution and architectures as applied to analysis, highlighting their application performance in different fields, including oncology, neurology, cardiology, pulmonology, ophthalmology, dermatology, orthopedics. The paper also explores challenges specific outlines trends future research directions. aims serve valuable resource for researchers practitioners healthcare artificial intelligence.

Язык: Английский

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An Efficient and Hybrid Deep Learning-Driven Model to Enhance Security and Performance of Healthcare Internet of Things

IEEE Access, Год журнала: 2025, Номер 13, С. 22931 - 22945

Опубликована: Янв. 1, 2025

Язык: Английский

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Differential evolution-driven optimized ensemble network for brain tumor detection

International Journal of Machine Learning and Cybernetics, Год журнала: 2025, Номер unknown

Опубликована: Апрель 16, 2025

Язык: Английский

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A review of multimodal fusion–based deep learning for Alzheimer’s disease

Neuroscience, Год журнала: 2025, Номер 576, С. 80 - 95

Опубликована: Апрель 25, 2025

Язык: Английский

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Optimized stacked long short-term memory with hyperbolic secant activation function for Alzheimer’s disease classification

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 108, С. 107980 - 107980

Опубликована: Апрель 29, 2025

Язык: Английский

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Investigating the relationship between brain age and Alzheimer’s disease: A deep learning approach with multimodal MRI

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 109, С. 107926 - 107926

Опубликована: Май 6, 2025

Язык: Английский

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A multi-layered defense against adversarial attacks in brain tumor classification using ensemble adversarial training and feature squeezing

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 14, 2025

Deep learning, particularly convolutional neural networks (CNNs), has proven valuable for brain tumor classification, aiding diagnostic and therapeutic decisions in medical imaging. Despite their accuracy, these models are vulnerable to adversarial attacks, compromising reliability clinical settings. In this research, we utilized a VGG16-based CNN model classify tumors, achieving 96% accuracy on clean magnetic resonance imaging (MRI) data. To assess robustness, exposed the Fast Gradient Sign Method (FGSM) Projected Descent (PGD) which reduced 32% 13%, respectively. We then applied multi-layered defense strategy, including training with FGSM PGD examples feature squeezing techniques such as bit-depth reduction Gaussian blurring. This approach improved resilience, 54% 47% examples. Our results highlight importance of proactive strategies maintaining AI under conditions.

Язык: Английский

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Computational Brain Imaging Framework for Neurological Mapping and Disorder Classification Using Multimodal Image Processing

International Journal of Computational Intelligence Systems, Год журнала: 2025, Номер 18(1)

Опубликована: Май 19, 2025

Язык: Английский

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