Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Фев. 19, 2025
Skin diseases, a significant category in the medical field, have always been challenging to diagnose and high misdiagnosis rate. Deep learning for skin disease classification has considerable value clinical diagnosis treatment. This study proposes model based on multi-scale channel attention. The network architecture of consists three main parts: an input module, four processing blocks, output module. Firstly, improved pyramid segmentation attention module extract features image entirely. Secondly, reverse residual structure is used replace backbone network, integrated into achieve better feature extraction. Finally, adaptive average pool fully connected layer, which convert aggregated global several categories generate final task. To verify performance proposed model, this two commonly datasets, ISIC2019 HAM10000, validation. experimental results showed that accuracy was 77.6 $$\%$$ series dataset 88.2 HAM10000 dataset. External validation data added evaluation validate further, comprehensive proved effectiveness paper.
Язык: Английский
Процитировано
1An intelligent framework for skin cancer detection and classification using fusion of Squeeze-Excitation-DenseNet with Metaheuristic-driven ensemble deep learning models
Scientific Reports, Год журнала: 2025, Номер 15(1)
Опубликована: Март 3, 2025
Skin cancer is the most dominant and critical method of cancer, which arises all over world. Its damaging effects can range from disfigurement to major medical expenditures even death if not analyzed preserved timely. Conventional models skin recognition require a complete physical examination by specialist, time-wasting in few cases. Computer-aided medicinal analytical methods have gained massive popularity due their efficiency effectiveness. This model assist dermatologists initial significant for early diagnosis. An automatic classification utilizing deep learning (DL) help doctors perceive kind lesion improve patient's health. The one hot topics research field, along with development DL structure. manuscript designs develops Detection Cancer Using an Ensemble Deep Learning Model Gray Wolf Optimization (DSC-EDLMGWO) method. proposed DSC-EDLMGWO relies on biomedical imaging. presented initially involves image preprocessing stage at two levels: contract enhancement using CLAHE noise removal wiener filter (WF) model. Furthermore, utilizes SE-DenseNet method, fusion squeeze-and-excitation (SE) module DenseNet extract features. For process, ensemble models, namely long short-term memory (LSTM) technique, extreme machine (ELM) model, stacked sparse denoising autoencoder (SSDA) employed. Finally, gray wolf optimization (GWO) optimally adjusts models' hyperparameter values, resulting more excellent performance. effectiveness approach evaluated benchmark database, outcomes measured across various performance metrics. experimental validation portrayed superior accuracy value 98.38% 98.17% under HAM10000 ISIC datasets other techniques.
Язык: Английский
Процитировано
0Melanoma Skin Cancer Recognition with a Convolutional Neural Network and Feature Dimensions Reduction with Aquila Optimizer
Diagnostics, Год журнала: 2025, Номер 15(6), С. 761 - 761
Опубликована: Март 18, 2025
Background: Melanoma is a highly aggressive form of skin cancer, necessitating early and accurate detection for effective treatment. This study aims to develop novel classification system melanoma that integrates Convolutional Neural Networks (CNNs) feature extraction the Aquila Optimizer (AO) dimension reduction, improving both computational efficiency accuracy. Methods: The proposed method utilized CNNs extract features from images, while AO was employed reduce dimensionality, enhancing performance model. effectiveness this hybrid approach evaluated on three publicly available datasets: ISIC 2019, ISBI 2016, 2017. Results: For 2019 dataset, model achieved 97.46% sensitivity, 98.89% specificity, 98.42% accuracy, 97.91% precision, 97.68% F1-score, 99.12% AUC-ROC. On 2016 it reached 98.45% 98.24% 97.22% 97.84% 97.62% 98.97% 2017, results were 98.44% 98.86% 97.96% 98.12% 97.88% 99.03% outperforms existing advanced techniques, with 4.2% higher 6.2% improvement in 5.8% increase specificity. Additionally, reduced complexity by up 37.5%. Conclusions: deep learning-Aquila (DL-AO) framework offers efficient detection, making suitable deployment resource-constrained environments such as mobile edge computing platforms. integration DL metaheuristic optimization significantly enhances robustness, detection.
Язык: Английский
Процитировано
0Skin Cancer Image Classification Using Artificial Intelligence Strategies: A Systematic Review
Journal of Imaging, Год журнала: 2024, Номер 10(11), С. 265 - 265
Опубликована: Окт. 22, 2024
The increasing incidence of and resulting deaths associated with malignant skin tumors are a public health problem that can be minimized if detection strategies improved. Currently, diagnosis is heavily based on physicians' judgment experience, which occasionally lead to the worsening lesion or needless biopsies. Several non-invasive imaging modalities, e.g., confocal scanning laser microscopy multiphoton microscopy, have been explored for cancer assessment, aligned different artificial intelligence (AI) assist in diagnostic task, several image features, thus making process more reliable faster. This systematic review concerns implementation AI methods tumor classification following PRISMA guidelines. In total, 206 records were retrieved qualitatively analyzed. Diagnostic potential was found techniques, particularly dermoscopy images, yielding results close perfection. Learning approaches support vector machines neural networks seem preferred, recent focus convolutional networks. Still, detailed descriptions training/testing conditions lacking some reports, hampering reproduction. use an expanding field, future work aiming construct optimal learning strategies. Ultimately, early could optimized, improving patient outcomes, even areas where healthcare scarce.
Язык: Английский
Процитировано
1A Deep Learning Approach to Classify Skin Cancer
Опубликована: Ноя. 23, 2024
Язык: Английский
Процитировано
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