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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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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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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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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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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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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Exploiting histopathological imaging for early detection of lung and colon cancer via ensemble deep learning model

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 3, 2024

Cancer seems to have a vast number of deaths due its heterogeneity, aggressiveness, and significant propensity for metastasis. The predominant categories cancer that may affect males females occur worldwide are colon lung cancer. A precise on-time analysis this can increase the survival rate improve appropriate treatment characteristics. An efficient effective method speedy accurate recognition tumours in areas is provided as an alternative methods. Earlier diagnosis disease on front drastically reduces chance death. Machine learning (ML) deep (DL) approaches accelerate diagnosis, facilitating researcher workers study majority patients limited period at low cost. This research presents Histopathological Imaging Early Detection Lung Colon via Ensemble DL (HIELCC-EDL) model. HIELCC-EDL technique utilizes histopathological images identify (LCC). To achieve this, uses Wiener filtering (WF) noise elimination. In addition, model channel attention Residual Network (CA-ResNet50) complex feature patterns. Moreover, hyperparameter selection CA-ResNet50 performed using tuna swarm optimization (TSO) technique. Finally, detection LCC achieved by ensemble three classifiers such extreme machine (ELM), competitive neural networks (CNNs), long short-term memory (LSTM). illustrate promising performance model, complete set experimentations was benchmark dataset. experimental validation portrayed superior accuracy value 99.60% over recent approaches.

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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Advanced brain tumor segmentation using DeepLabV3Plus with Xception encoder on a multi-class MR image dataset

Multimedia Tools and Applications, Год журнала: 2025, Номер unknown

Опубликована: Фев. 21, 2025

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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