Revolutionizing Dermatology: A Comprehensive Survey of AI-Enhanced Early Skin Cancer Diagnosis

Archives of Computational Methods in Engineering, Год журнала: 2024, Номер unknown

Опубликована: Апрель 23, 2024

Язык: Английский

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Melanoma identification and classification model based on fine-tuned convolutional neural network

Digital Health, Год журнала: 2024, Номер 10

Опубликована: Янв. 1, 2024

Breakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing that can be lethal to humans. For instance, melanoma is the most unpredictable terrible form of cancer.

Язык: Английский

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A Comprehensive Survey on Federated Learning in the Healthcare Area: Concept and Applications

Computer Modeling in Engineering & Sciences, Год журнала: 2024, Номер 140(3), С. 2239 - 2274

Опубликована: Янв. 1, 2024

Federated learning is an innovative machine technique that deals with centralized data storage issues while maintaining privacy and security.It involves constructing models using datasets spread across several centers, including medical facilities, clinical research Internet of Things devices, even mobile devices.The main goal federated to improve robust benefit from the collective knowledge these disparate without centralizing sensitive information, reducing risk loss, breaches, or exposure.The application in healthcare industry holds significant promise due wealth generated various sources, such as patient records, imaging, wearable surveys.This conducts a systematic evaluation highlights essential for selection implementation approaches healthcare.It evaluates effectiveness strategies field offers analysis domain, encompassing metrics employed.In addition, this study increasing interest applications among scholars provides foundations further studies.

Язык: Английский

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Combining State-of-the-Art Pre-Trained Deep Learning Models: A Noble Approach for Skin Cancer Detection Using Max Voting Ensemble

Diagnostics, Год журнала: 2023, Номер 14(1), С. 89 - 89

Опубликована: Дек. 30, 2023

Skin cancer poses a significant healthcare challenge, requiring precise and prompt diagnosis for effective treatment. While recent advances in deep learning have dramatically improved medical image analysis, including skin classification, ensemble methods offer pathway further enhancing diagnostic accuracy. This study introduces cutting-edge approach employing the Max Voting Ensemble Technique robust classification on ISIC 2018: Task 1-2 dataset. We incorporate range of cutting-edge, pre-trained neural networks, MobileNetV2, AlexNet, VGG16, ResNet50, DenseNet201, DenseNet121, InceptionV3, ResNet50V2, InceptionResNetV2, Xception. These models been extensively trained datasets, achieving individual accuracies ranging from 77.20% to 91.90%. Our method leverages synergistic capabilities these by combining their complementary features elevate performance further. In our approach, input images undergo preprocessing model compatibility. The integrates with architectures weights preserved. For each lesion under examination, every produces prediction. are subsequently aggregated using max voting technique yield final majority-voted class serving as conclusive Through comprehensive testing diverse dataset, outperformed models, attaining an accuracy 93.18% AUC score 0.9320, thus demonstrating superior reliability evaluated effectiveness proposed HAM10000 dataset ensure its generalizability. delivers robust, reliable, tool cancer. By utilizing power advanced we aim assist professionals timely accurate diagnoses, ultimately reducing mortality rates patient outcomes.

Язык: Английский

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A hierarchical sparrow search algorithm to solve numerical optimization and estimate parameters of carbon fiber drawing process

Artificial Intelligence Review, Год журнала: 2023, Номер 56(S1), С. 1113 - 1148

Опубликована: Июль 19, 2023

Язык: Английский

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