Open AccessDistributed densely connected convolutional network approach on patient’s metadata of dermoscopic images for early melanoma detection
Author(s) -
Nallanti Venkateswararao,
P. Venkateswara Rao
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns2.6365
Subject(s) - computer science , metadata , artificial intelligence , feature (linguistics) , similarity (geometry) , convolutional neural network , pattern recognition (psychology) , convolution (computer science) , melanoma , deep learning , skin lesion , image (mathematics) , medicine , dermatology , artificial neural network , philosophy , linguistics , cancer research , operating system
Melanoma is one of the most deadly cancers on the planet, and it has the ability to spread to new places of the body if not discovered early enough. Dermoscopic pictures are routinely used to identify melanoma. Many previous studies, based on standard classification approaches and deep learning models, have been proposed for automated analysis of skin lesions. In traditional classification systems, handcrafted functions are used as input. However, due to the high visual similarity between different classes of skin lesions and complex skin diseases, hand-made features are not discriminating enough and fail in many circumstances. Convolutional networks with fewer connections between the input layers and those close to the output can be significantly deeper, more accurate and more effective for training, according to a recent study. In this study, we accept and offer the application of Hadoop Distributed Tight Convolution Network (HdiDenseNet) for melanoma skin cancer detection and study of failed lesions, which can be connected to the mode of transmission. You can use all feature maps at all input levels as input, and your own maps will be used as inputs in all poster headers.