Computer Science · Literature

Research papers on Neural networks for image recognition

Recent and highly-cited academic work on neural networks for image recognition, gathered from Semantic Scholar, CrossRef and OpenAlex.

1. A patch-based convolutional neural network for remote sensing image classification

   Atharva Sharma, Xiuwen Liu, Xiaojun Yang, et al. · 2017 · Neural Networks · 228 citations

2. Text/non-text image classification in the wild with convolutional neural networks

   Xiang Bai, Baoguang Shi, Chengquan Zhang, et al. · 2017 · Pattern Recognition · 98 citations

3. String representations and distances in deep Convolutional Neural Networks for image classification

   Cécile Barat, Christophe Ducottet · 2016 · Pattern Recognition · 54 citations

4. Inductive conformal predictor for convolutional neural networks: Applications to active learning for image classification

   Sergio Matiz, Kenneth E. Barner · 2019 · Pattern Recognition · 45 citations

5. Improving the Performance of Convolutional Neural Networks for Image Classification

   Davar Giveki · 2021 · Optical Memory and Neural Networks · 14 citations

6. Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

   Maissa HAMOUDA, Med Salim BOUHLEL · 2025 · IET Image Processing · 7 citations

   Abstract Classification of Hyperspectral Satellite Images (HSI) is a very important technology for object detection and cartography. Several problems can be detected, which make classification difficult (large size of the images, fusion between the classes, small amount of samples, etc.). Recently, several Convolutional Neural Networks (CNN‐HSI) have been proposed for the classification of hyperspectral images. In this article, an improvement to CNN‐HSI is proposed, aiming to reduce the number of erroneous pixels during classification (due to the limited number of samples). Thus, an extra‐convolution technique (ExCNN) is proposed, where we add layers of global convolutions

7. Creating Deep Convolutional Neural Networks for Image Classification

   Nabeel Siddiqui · 2023 · Programming Historian · 2 citations

   This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google's Teachable Machine to train a model on paintings from the ArtUK database. This lesson also demonstrates how to use Javascript to embed the model in a live website.

8. Application of convolutional neural networks in image classification and applications of improved convolutional neural networks

   Taoyu Liu · 2024 · Applied and Computational Engineering · 2 citations

   This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised model is compared with the conventional VGG-16 CNN architecture in the paper. Next, the application of sparse co

9. Application of Deep Convolutional Neural Networks in Image Recognition and Classification in Library Management

   Songyun Wang · 2023 · Wireless Personal Communications · 1 citations

10. Improvement of Convolutional Neural Networks in Image Classification and Recognition

    Pengju Xia · 2025 · International Journal of Innovative Computing and Applications