Skin cancer detection from dermoscopic images using Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm

Biomedical Signal Processing and Control, Год журнала: 2023, Номер 86, С. 105264 - 105264

Опубликована: Июль 20, 2023

Язык: Английский

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Melanoma Diagnosis Using Enhanced Faster Region Convolutional Neural Networks Optimized by Artificial Gorilla Troops Algorithm

Information Technology And Control, Год журнала: 2023, Номер 52(4), С. 819 - 832

Опубликована: Дек. 22, 2023

Melanoma, a rapidly spreading and perilous type of skin cancer, is the focus this study, presenting reliable technique for its detection. It one most prevalent types cancer that might be challenging medical professionals to diagnose. Artificial intelligence can improve diagnostic accuracy when utilized in conjunction with expertise specialists. An innovative computer-aided method diagnosis has been introduced current study. The construction proposed uses African Gorilla Troops Optimizer (AGTO) Algorithm, recently meta-heuristic optimization algorithm, deep learning models such as Faster Region Convolutional Neural Networks. To reduce complexity analytic process, valuable features are chosen using AGTO method, further classification implemented R-CNN. model applied ISIC-2020 dataset. When final performance results from compared those four existing works, findings show system outperforms an 98.55%.

Язык: Английский

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An efficient computer-aided diagnosis model for classifying melanoma cancer using fuzzy-ID3-pvalue decision tree algorithm

Multimedia Tools and Applications, Год журнала: 2024, Номер 83(31), С. 76731 - 76751

Опубликована: Фев. 20, 2024

Язык: Английский

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Pipelined Structure in the Classification of Skin Lesions Based on Alexnet CNN and SVM Model With Bi-Sectional Texture Features

IEEE Access, Год журнала: 2024, Номер 12, С. 57366 - 57380

Опубликована: Янв. 1, 2024

The classification of skin lesions is crucial because it increases the likelihood that malignant will be discovered early on, allowing for more effective treatment. Due to abundance lesion images and possibility human error, detection can difficult dermatologists. This work aims classify using two pipelines were designed support vector machine (SVM) AlexNet convolutional neural network (CNN) models. Pipeline-1 uses CNN, while pipeline-2 proposes a bisectional feature extraction approach with an SVM model. are initially preprocessed regions segmented. further subdivided into four based on intensity mapping function. features then extracted from trained dataset used in experiment HAM-10000 PAD-UFES-20 dataset, which consists dermatoscopic images. Based models' accuracy, sensitivity, DCI, specificity, F1-score, experiment's findings assessed five different conditions. By accurately effectively classifying lesions, study's help diagnosis treatment disorders. pipeline performs better than CNN where result accuracy 98.66% 97.68% respectively dataset. structure results 96.87% 98.10%

Язык: Английский

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Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010–2023)

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

Опубликована: Июнь 24, 2024

Abstract Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and presents a high risk of disability, morbidity, mortality among older adults. However, predictions based on sequential neural network SO studies the relationship between physical fitness factors are lacking. This study aimed to develop predictive model for in adults focusing factors. A comprehensive dataset Korean participating national programs was analyzed using networks. Appendicular skeletal muscle/body weight defined as an anthropometric equation. Independent variables included body fat (BF, %), waist circumference, systolic diastolic blood pressure, various The dependent variable binary outcome (possible vs normal). We hyperparameter tuning stratified K-fold validation optimize model. prevalence significantly higher women (13.81%) than men, highlighting sex-specific differences. optimized Shapley Additive Explanations analysis demonstrated accuracy 93.1%, with BF% absolute grip strength emerging most influential predictors SO. highly accurate adults, emphasizing critical roles strength. identified BF, strength, sit-and-reach key predictors. Our findings underscore nature importance its prediction.

Язык: Английский

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