A novel CNN-ViT-based deep learning model for early skin cancer diagnosis

Biomedical Signal Processing and Control, Год журнала: 2025, Номер 104, С. 107627 - 107627

Опубликована: Янв. 28, 2025

Язык: Английский

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A systematic review of deep learning in MRI-based cerebral vascular occlusion-based brain diseases

Neuroscience, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

Язык: Английский

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Quantum computational infusion in extreme learning machines for early multi-cancer detection

Journal Of Big Data, Год журнала: 2025, Номер 12(1)

Опубликована: Фев. 6, 2025

Язык: Английский

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Comparison of deep transfer learning models for classification of cervical cancer from pap smear images

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

Опубликована: Янв. 31, 2025

Язык: Английский

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Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images

Computers in Biology and Medicine, Год журнала: 2025, Номер 188, С. 109790 - 109790

Опубликована: Фев. 13, 2025

Язык: Английский

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An Innovative Deep Learning Framework for Skin Cancer Detection Employing ConvNeXtV2 and Focal Self-Attention Mechanisms

Results in Engineering, Год журнала: 2024, Номер unknown, С. 103692 - 103692

Опубликована: Дек. 1, 2024

Язык: Английский

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Enhanced hybrid attention deep learning for avocado ripeness classification on resource constrained devices

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

Опубликована: Янв. 29, 2025

Attention mechanisms such as the Convolutional Block Module (CBAM) can help emphasize and refine most relevant feature maps color, texture, spots, wrinkle variations for avocado ripeness classification. However, CBAM lacks global context awareness, which may prevent it from capturing long-range dependencies or patterns relationships between distant regions in image. Further, more complex neural networks improve model performance but at cost of increasing number layers train parameters, not be suitable resource constrained devices. This paper presents Hybrid Neural Network (HACNN) classifying on It aims to perform local enhancement capture relationships, leading a comprehensive extraction by combining attention modules models. The proposed HACNN combines transfer learning with hybrid mechanisms, including Spatial, Channel, Self-Attention Modules, effectively intricate features fourteen thousand images. Extensive experiments demonstrate that EfficienctNet-B3 significantly outperforms conventional models regarding accuracy 96.18%, 92.64%, 91.25% train, validation, test models, respectively. In addition, this consumed 59.81 MB memory an average inference time 280.67 ms TensorFlow Lite smartphone. Although ShuffleNetV1 (1.0x) consumes least resources, its testing is only 82.89%, insufficient practical applications. Thus, MobileNetV3 Large exciting option has 91.04%, usage 26.52 MB, 86.94 These findings indicated method enhances classification ensures feasibility implementation low-resource environments.

Язык: Английский

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Explainable label guided lightweight network with axial transformer encoder for early detection of oral cancer

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

Опубликована: Фев. 21, 2025

Oral cavity cancer exhibits high morbidity and mortality rates. Therefore, it is essential to diagnose the disease at an early stage. Machine learning convolution neural networks (CNN) are powerful tools for diagnosing mouth oral cancer. In this study, we design a lightweight explainable network (LWENet) with label-guided attention (LGA) provide second opinion expert. The LWENet contains depth-wise separable layers reduce computation costs. Moreover, LGA module provides label consistency neighbor pixel improves spatial features. Furthermore, AMSA (axial multi-head self-attention) based ViT encoder incorporated in model global attention. Our (vision transformer) computationally efficient compared classical encoder. We tested LWRNet performance on MOD (mouth disease) OCI (oral image) datasets, results other CNN methods. achieved precision F1-scores of 96.97% 98.90% dataset, 99.48% 98.23% respectively. By incorporating Grad-CAM, visualize decision-making process, enhancing interpretability. This work demonstrates potential facilitating detection.

Язык: Английский

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Advanced deep learning model for crop-specific and cross-crop pest identification

Expert Systems with Applications, Год журнала: 2025, Номер 274, С. 126896 - 126896

Опубликована: Фев. 24, 2025

Язык: Английский

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Utilizing convolutional neural networks and vision transformers for precise corn leaf disease identification

Neural Computing and Applications, Год журнала: 2024, Номер unknown

Опубликована: Дек. 5, 2024

Язык: Английский

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