CNN architecture and three preconfigured models(AlexNet, ResNet, and InceptionV3) are part of the proposed deep learning system. For the purpose of Skin Disease Classification, a Dataset of photos featuring seven disorders has been collected. Melanoma, nevi, seborrhoea keratosis, and other skin ...
Nevertheless, the nature of the skin cancer lesion is very complex, and developing an automated diagnosis system using deep learning is challenging. To alleviate this problem, transfer learning is used. In our study, EfficientNetB3 is used for skin cancer detection. EfficientNetB3 is an up-to-...
Transfer Learning with DCNNs (DenseNet, Inception V3, Inception-ResNet V2, VGG16) for skin lesions classification on HAM10000 dataset largescale data. skinskin-segmentationskin-detectionskin-cancerskin-diseaseskin-lesion-classificationskin-lesion-segmentationskin-cancer-detectionskin-disease-classifiction ...
In this study, we address the challenge of enhancing the efficiency of skin cancer detection models by introducing an approach, illustrated in Fig. 1, that integrates knowledge distillation, multi-teacher knowledge distillation, model souping, and ensembling techniques. Leveraging ResNet50 and DenseNet...
In this subsection, we compare the performance of different versions of LSATrans with the aforementioned baseline methods in the task of common skin disease classification using SKIN-CLS dataset. The experimental results are presented in Fig. 2. Among the CNN-based baselines, RegNet_3200m demonstrat...
Dermatological conditions are a relevant health problem. Machine learning (ML) models are increasingly being applied to dermatology as a diagnostic decision support tool using image analysis, especially for skin cancer detection and disease classificatio
Skin cancer is a life-threatening disease caused by unstable division and proliferation of cells under the skin. Many reasons trigger this disease. The most important one; exposure to the harmful effects of ultraviolet rays or genetic transmission. Therefore, early detection is important in skin can...
Deep learning has been widely used in the medical field for disease detection and classification. Therefore, this study leverages DenseNet deep learning models for LSDV detection and classifica- tion. Experiments are performed using VGG-16, ResNet-50, MobileNet-V2, custom-designed ...
Using a SoftMax activation function in the last dense layer, a composite feature vector created by these CNN models is then used to classify skin cancer. Our proposed federated learning approach used five different deep learning models with nine different skin cancer datasets for the detection of ...
Skin Cancer, which leads to a large number of deaths annually, has been extensively considered as the most lethal tumor around the world. Accurate detection of skin cancer in its early stage can significantly raise the survival rate of patients and reduc