The experiments show that the proposed approach improves the final classifier invariance for common melanoma variations, common skin patterns and markers.
Oct 2, 2024 � Highlights•A ternary classifier is proposed including patterns surrounding the lesion.•Oversampling of the malignant class is investigated�...
Jul 26, 2024 � The present review aimed to screen the scientific literature on the application of DL techniques to dermoscopic melanoma/nevi differential�...
Recent results show that deep learning based approaches learn from data, and can outperform human specialists in a set of tasks when large databases are�...
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Aug 26, 2024 � This study aims to explore the efficacy of a hybrid deep learning and radiomics approach, supplemented with patient metadata, in the noninvasive dermoscopic�...
Missing: Experiments | Show results with:Experiments
The SqueezeNet deep learning model was trained using the enhanced images. The experiments revealed that the accuracy of melanoma identification improved�...
In particular, our approach was 93% accurate in identifying the presence or absence of melanoma, with sensitivities and specificities in the. 86%–94% range.
This paper presents a Computer Assisted Diagnosis (CAD) framework that has been developed to detect skin illnesses at an early stage.
Oct 28, 2022 � In the proposed work, Convolutional Neural Networks (CNN) and Machine Learning algorithms are used to accurately classify pigmented skin lesions�...
May 22, 2023 � Applying DL methods in skin lesion classification helps automate the screening and early diagnosis of skin cancer, even in areas without easy�...