Convolutional Neural Networks for classifying skin lesions

Pai, K and Giridharan, A (2019) Convolutional Neural Networks for classifying skin lesions. In: 2019 IEEE Region 10 Conference: Technology, Knowledge, and Society, TENCON 2019, 17-20 October 2019, Kochi, India, India, pp. 1794-1796.

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Abstract

The usage of Deep Learning has immensely increased in the present years. Convolutional Neural Networks, Recurrent Neural Networks, Generative Adversarial Networks, Variational Auto Encoders are among the prominent architectures in Deep Learning. Convolutional Neural Networks architecture has signified high accuracy and performance for image classification problems. On the other hand skin cancer if recognized or treated early is almost curable. The proposed model in the paper uses Convolutional Neural Networks to predict and classify seven different types of skin lesions. A website is developed for the real time usage of the model, which can predict the three most probable types of skin lesions for a given image. The observations and results are based on the experiment conducted using the MNIST:HAM10000 dataset which consists of 10000 labelled images.

Item Type:	Conference Paper
Publication:	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	Copyright of this article belongs to IEEE
Keywords:	Computer vision; Convolution; Deep learning; Deep neural networks; Dermatology; Network architecture, Adversarial networks; Auto encoders; Convolutional neural network; High-accuracy; Real time; Skin cancers; Skin lesion, Recurrent neural networks
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited:	03 Mar 2020 08:42
Last Modified:	03 Mar 2020 08:42
URI:	http://eprints.iisc.ac.in/id/eprint/64438

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