Use of CNNs for the Classification of Medical Images
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Sprache:Englisch
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Erscheinungsdatum
01.06.2022
Verlag
GRINSeitenzahl
29 (Printausgabe)
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1678 KB
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1. Auflage
Sprache
Englisch
EAN
9783346653963
Project Report from the year 2021 in the subject Computer Sciences - Artificial Intelligence, grade: 17/20, University of Poitiers, language: English, abstract: This research project structures different enhanced architectures and models of CNNs using in particular the VGG16 model, for its featured simplicity and efficiency along with its pre-trained wights on ImageNet. The VGG16 models are well trained using transfer learning mechanism in fine-tuning the architecture on the ISIC2018 Task3 dataset. Then, the models are projected for skin cancer image classification in highlighting the state-of-the-art performance.
Deep learning models have showed great capabilities in data modelling on the various applications of image processing, including segmentation, classification, tagging, and many others. In particular, convolutional neural network (CNNs) has proved to be effective in capturing deep features on unstructured data that are well sited in the state-of-the-art. It is well competitive in comparison to the traditional algorithms of machine learning.
Deep learning models have showed great capabilities in data modelling on the various applications of image processing, including segmentation, classification, tagging, and many others. In particular, convolutional neural network (CNNs) has proved to be effective in capturing deep features on unstructured data that are well sited in the state-of-the-art. It is well competitive in comparison to the traditional algorithms of machine learning.
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