- MACHINE LEARNING
- CONVOLUTIONAL NEURAL NETWORKS
- DEEP LEARNING
- IMAGE PROCESSING
- MALIGNANT MELANOMA
- ARTIFCIAL INTELLIGENCE
- ARTIFICIAL NEURAL NETWORKS
- DECISION SUPPORT
- DECISION SUPPORT, DIAGNOSTICS, IMAGE PROCESSING, ARTIFICIAL NEURAL NETWORKS, ENSEMBLE OF NEURAL NETWORKS, MELANOMA MALIGNANT
- DEEP CONVOLUTIONAL NEURAL NETWORK
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...
Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy
The diabetic retinopathy is a disease caused by long-standing diabetes. Lack of effective treatment can lead to vision impairment and even irreversible blindness. The disease can be diagnosed by examining digital color fundus photographs of retina. In this paper we propose deep learning approach to automated diabetic retinopathy screening. Deep convolutional neural networks (CNN) - the most popular kind of deep learning algorithms...
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