Performance Evaluation on Category Classification of an Images Using Convnet

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K. Sheikdavood, K. Prabhu, S. Allirani


To category, unknown things sometimes a very difficult task for a human and also machine in required commercial purposes. Unknown things like flowers, food, animals, medical modalities, and items that are not very known are unable to identify by us. The main objective of this work is helping the people who very poor in identifying things can do better with this system. Even for kids, we can train them from childhood as a PlayStation to make them master in their knowledge by using this system. This is a pre-trained deep learning system with more than 500 images as a database in each specific category.  When a test image has been loaded it can give us test data belong to which category. This makes people know about unknown things with the highest efficiency. This system classified all the test data with efficiency above 97 %. The pre-trained deep learning system evolves with convolutional neural networks. It can convolve with all the features of the training image and forming the layers to provide an efficient classifier system for commercial purposes.

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K. Sheikdavood, K. Prabhu, S. Allirani. (2021). Performance Evaluation on Category Classification of an Images Using Convnet. Annals of the Romanian Society for Cell Biology, 2202–2208. Retrieved from