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Disease Classification within Dermascopic Images Using features extracted by ResNet50 and classification through Deep Forest
In this report we propose a classification technique for skin lesion ima...
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Automated Phenotyping of Epicuticular Waxes of Grapevine Berries Using Light Separation and Convolutional Neural Networks
In viticulture the epicuticular wax as the outer layer of the berry skin...
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Measles Rash Image Detection Using Deep Convolutional Neural Network
Measles is extremely contagious and is one of the leading causes of vacc...
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Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet
Skin cancer, a major form of cancer, is a critical public health problem...
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Properties Of Winning Tickets On Skin Lesion Classification
Skin cancer affects a large population every year – automated skin cance...
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Diagnostic Accuracy of Content Based Dermatoscopic Image Retrieval with Deep Classification Features
Background: Automated classification of medical images through neural ne...
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Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement
Automated pavement crack detection and measurement are important road is...
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Skin lesion detection based on an ensemble of deep convolutional neural network
Skin cancer is a major public health problem, with over 5 million newly diagnosed cases in the United States each year. Melanoma is the deadliest form of skin cancer, responsible for over 9,000 deaths each year. In this paper, we propose an ensemble of deep convolutional neural networks to classify dermoscopy images into three classes. To achieve the highest classification accuracy, we fuse the outputs of the softmax layers of four different neural architectures. For aggregation, we consider the individual accuracies of the networks weighted by the confidence values provided by their final softmax layers. This fusion-based approach outperformed all the individual neural networks regarding classification accuracy.
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