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Deep CNN architectures for learning image classification: A systematic review, taxonomy and open challenges
| Content Provider | Scilit |
|---|---|
| Author | Nagaraju, M. Chawla, Priyanka |
| Copyright Year | 2021 |
| Description | Object detection and object classification have driven many of the advances in modeling deep learning models. Nowadays most of the research on deep convolutional neural networks or deep CNNs has been focusing on efficiency while recognizing the objects and their classification in an input image. This process can contribute to the early detection of diseases in humans and plants with a higher level of accuracy. The deep CNN models are the learning approaches in the field of computer vision and machine learning. The present review article has examined some of the existing deep learning techniques that are used to process, detect and classify the input data with eminence on detecting the diseases. First, a review of deep CNN models based on their architectures is provided. Secondly, the study highlighted and explored some of the current implementation challenges and future directions. Finally, the article concluded with the learning capabilities 122by implementing the deep CNN models while detecting the image objects and their classification accuracy. Book Name: Artificial Intelligence and Speech Technology |
| Related Links | https://api.taylorfrancis.com/content/chapters/edit/download?identifierName=doi&identifierValue=10.1201/9781003150664-14&type=chapterpdf |
| Ending Page | 128 |
| Page Count | 8 |
| Starting Page | 121 |
| DOI | 10.1201/9781003150664-14 |
| Language | English |
| Publisher | Informa UK Limited |
| Publisher Date | 2021-06-24 |
| Access Restriction | Open |
| Subject Keyword | Book Name: Artificial Intelligence and Speech Technology Computer Science Classification Neural Architectures Cnn Models Deep Cnn Detection |
| Content Type | Text |
| Resource Type | Chapter |
