Application of Deep Convulational Neural Network in Medical Image Classification

Autor:	Vaishali Sugat Pawar, Sitaram B. More, Rahul Rathod, Samir S. Yadav, Sugat Ramrao Pawar
Rok vydání:	2021
Předmět:	Artificial neural network Contextual image classification Computer science business.industry Machine learning computer.software_genre Training methods Convolutional neural network Residual neural network Convolution Artificial intelligence Architecture business Focus (optics) computer
Zdroj:	2021 International Conference on Emerging Smart Computing and Informatics (ESCI).
DOI:	10.1109/esci50559.2021.9396854
Popis:	The convolution networks make a great success in image classification since AlexNet proposed for classification in ImageNet challenge. This End-to-End training method makes the researcher focus more on how to build an efficient network. The user focuses more on how to use convolution networks in their application. After AlexNet, there are much ConvNet architecture proposed, such as VGGNet and ResNet. These ConvNet architectures are widely used in many actual real applications. Different types of techniques for image classification and many other medical applications focused on Convolutional Neural Networks(CNN) are addressed in this paper. So this paper will help us in selecting an appropriate classification technique among all the available methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6e16bb1444f579776162ce96e9bc1d1c https://doi.org/10.1109/esci50559.2021.9396854 Zobrazit plný text záznamu