Please use this identifier to cite or link to this item:
https://dspace.univ-ouargla.dz/jspui/handle/123456789/29035
Title: | Medical Image Classification with Convolutional Neural Network |
Authors: | Naimi, Mohamed Chouaib Naimi, Mohamed Amine Benatallah, Mohammed Tewfik |
Keywords: | convolutional neural network Artificial neural network DEEP LEARNING: |
Issue Date: | 16-Sep-2021 |
Publisher: | UNIVERSITY OF KASDI MERBAH OUARGLA |
Abstract: | Image patch categorization is a critical job in a wide range of medical imaging applications. We created a customized Convolutional Neural Network (CNN) with a shallow convolution layer to categorize lung image patches with interstitial lung disease in this study (ILD). Despite the fact that numerous feature descriptors have been developed in recent years, they can be extremely complex and domain-specific. Our customized CNN framework, on the other hand, can learn the intrinsic image characteristics from lung image patches that are most appropriate for classification automatically and effectively. The same architecture may be used to classify medical images or textures in various ways. |
URI: | http://dspace.univ-ouargla.dz/jspui/handle/123456789/29035 |
Appears in Collections: | Département d'Electronique et des Télécommunications - Master |
Files in This Item:
File | Description | Size | Format | |
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Naimi-Benatallah-Naimi.pdf | Automatic | 2,18 MB | Adobe PDF | View/Open |
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