mgr inż. Arkadiusz Kwasigroch
Słowa kluczowe Pomoc
- DEEP LEARNING
- MACHINE LEARNING
- CONVOLUTIONAL NEURAL NETWORKS
- IMAGE PROCESSING
- MALIGNANT MELANOMA
- ARTIFCIAL INTELLIGENCE
- ARTIFICIAL NEURAL NETWORKS
- BATCH NORMALIZATION, TRANSFER LEARNING, DROPOUT
- DECISION SUPPORT
- DECISION SUPPORT, DIAGNOSTICS, IMAGE PROCESSING, ARTIFICIAL NEURAL NETWORKS, ENSEMBLE OF NEURAL NETWORKS, MELANOMA MALIGNANT
The paper proposes an approach to designing the neuro-genetic self-learning decision support system. The system is based on neural networks being adaptively learned by evolutionary mechanism, forming an evolved neural network. Presented learning algorithm enables for a selection of the neural network structure by establishing or removing of connections between the neurons, and then for a finding the beast suited values of the network...
DIAGNOSIS OF MALIGNANT MELANOMA BY NEURAL NETWORK ENSEMBLE-BASED SYSTEM UTILISING HAND-CRAFTED SKIN LESION FEATURES
Malignant melanomas are the most deadly type of skin cancer but detected early have high chances for successful treatment. In the last twenty years, the interest of automated melanoma recognition detection and classification dynamically increased partially because of public datasets appearing with dermatoscopic images of skin lesions. Automated computer-aided skin cancer detection in dermatoscopic images is a very challenging task...
In recent years, deep learning and especially Deep Neural Networks (DNN) have obtained amazing performance on a variety of problems, in particular in classification or pattern recognition. Among many kinds of DNNs, the Convolutional Neural Networks (CNN) are most commonly used. However, due to their complexity, there are many problems related but not limited to optimizing network parameters, avoiding overfitting and ensuring good...
wyświetlono 351 razy