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Research papers on Neural networks for image recognition

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  2. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

    Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, et al. · 2016 · IEEE transactions on medical imaging · 5,171 citations

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer

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  4. Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

    Srdjan Sladojevic, M. Arsenović, A. Anderla, et al. · 2016 · Computational Intelligence and Neuroscience · 1,918 citations

    The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. A

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  5. Bag of Tricks for Image Classification with Convolutional Neural Networks

    Tong He, Zhi Zhang, Hang Zhang, et al. · 2018 · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 1,656 citations

    Much of the recent progress made in image classification research can be credited to training procedure refinements, such as changes in data augmentations and optimization methods. In the literature, however, most refinements are either briefly mentioned as implementation details or only visible in source code. In this paper, we will examine a collection of such refinements and empirically evaluate their impact on the final model accuracy through ablation study. We will show that, by combining these refinements together, we are able to improve various CNN models significantly. For example, we raise ResNet-50's top-1 validation accuracy from 75.3% to 79.29% on ImageNet. We will also demonstra

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  8. Review of Image Classification Algorithms Based on Convolutional Neural Networks

    Leiyu Chen, Shaobo Li, Q. Bai, et al. · 2021 · Remote. Sens. · 741 citations

    Image classification has always been a hot research direction in the world, and the emergence of deep learning has promoted the development of this field. Convolutional neural networks (CNNs) have gradually become the mainstream algorithm for image classification since 2012, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. In the wake of these successes, CNN-based methods have emerged in remote sensing image scene classification and achieved advanced classification accuracy. In this review, which focuses on the application of

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  9. Convolutional neural networks for hyperspectral image classification

    Shiqi Yu, Sen Jia, Chun-Yan Xu · 2017 · Neurocomputing · 618 citations

    As a powerful visual model, convolutional neural networks (CNNs) have demonstrated remarkable performance in various visual recognition problems, and attracted considerable attention in recent years. However, due to the highly correlated bands and insufficient training samples of hyperspectral image data, it still remains a challenging problem to effectively apply the CNN models on hyperspectral images. In this paper, an efficient CNN architecture has been proposed to boost its discriminative capability for hyperspectral image classification, in which the original data is used as the input and the final CNN outputs are the predicted class-related results. The proposed CNN infrastructure has

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  11. Hierarchical convolutional neural networks for fashion image classification

    Yian Seo, K. Shin · 2019 · Expert Syst. Appl. · 201 citations

    Abstract Deep learning can be applied in various business fields for better performance. Especially, fashion-related businesses have started to apply deep learning techniques on their e-commerce such as apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The most important backbone of these applications is the image classification task. However, apparel classification can be difficult due to its various apparel properties, and complexity in the depth of categorization. In other words, multi-class apparel classification can be hard and ambiguous to separate among similar classes. Here, we find the need of image classification reflecting hierarchical

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  12. A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks

    Malusi Sibiya, M. Sumbwanyambe · 2019 · AgriEngineering · 196 citations

    Plant leaf diseases can affect plant leaves to a certain extent that the plants can collapse and die completely. These diseases may drastically decrease the supply of vegetables and fruits to the market, and result in a low agricultural economy. In the literature, different laboratory methods of plant leaf disease detection have been used. These methods were time consuming and could not cover large areas for the detection of leaf diseases. This study infiltrates through the facilitated principles of the convolutional neural network (CNN) in order to model a network for image recognition and classification of these diseases. Neuroph was used to perform the training of a CNN network that recog

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  13. Convolution neural network in precision agriculture for plant image recognition and classification

    Halimatu Sadiyah Abdullahi, Ray E. Sheriff, F. Mahieddine · 2017 · International Conference on Innovative Computing Technology · 129 citations

    Agriculture is essential to the continued existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Precision Agriculture is thought to be solution required to achieve the production rate required. There has been a significant improvement in the area of image processing and data processing which has being a major challenge previously in the practice of precision Agriculture. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for diff

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  14. Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks

    Sarah Zuhair Kurdi, M. H. Ali, Hussein Mussa Muafa, et al. · 2023 · Journal of Personalized Medicine · 121 citations

    The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region proc

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  16. A Survey on Convolutional Neural Networks and Their Performance Limitations in Image Recognition Tasks

    Gabriela Rangel, J. Cuevas-Tello, J. Nuñez-Varela, et al. · 2024 · Journal of Sensors · 76 citations

    Convolutional neural networks (CNNs) have shown outstanding image classification performance, having been successfully applied in several real‐world applications. However, there are performance limitations encountered in CNNs and few works have pointed out these limitations across the literature. Therefore, this survey aims to provide a comprehensive explanation of both the importance and performance limitations of CNNs on image classification tasks. In this paper, we start with a brief CNN background and follow the main CNN architectures along with their contributions and benchmark datasets. We propose a classification of the CNNs performance limitations, which are grouped into four categor

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  17. Hybrid quantum-classical-quantum convolutional neural networks

    Changzhou Long, Meng Huang, Xiu-Cai Ye, et al. · 2025 · Scientific Reports · 76 citations

    Deep learning has achieved significant success in pattern recognition, with convolutional neural networks (CNNs) serving as a foundational architecture for extracting spatial features from images. Quantum computing provides an alternative computational framework, a hybrid quantum-classical convolutional neural networks (QCCNNs) leverage high-dimensional Hilbert spaces and entanglement to surpass classical CNNs in image classification accuracy under comparable architectures. Despite performance improvements, QCCNNs typically use fixed quantum layers without incorporating trainable quantum parameters. This limits their ability to capture non-linear quantum representations and separates the mod

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  19. AAtt-CNN: Automatic Attention-Based Convolutional Neural Networks for Hyperspectral Image Classification

    Mercedes Eugenia Paoletti, S. Moreno-Álvarez, Yu Xue, et al. · 2023 · IEEE Transactions on Geoscience and Remote Sensing · 51 citations

    Convolutional models have provided outstanding performance in the analysis of hyperspectral images (HSIs). These architectures are carefully designed to extract intricate information from nonlinear features for classification tasks. Notwithstanding their results, model architectures are manually engineered and further optimized for generalized feature extraction. In general terms, deep architectures are time-consuming for complex scenarios, since they require fine-tuning. Neural architecture search (NAS) has emerged as a suitable approach to tackle this shortcoming. In parallel, modern attention-based methods have boosted the recognition of sophisticated features. The search for optimal neur

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  20. Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

    Liangji Zhou, Qingwu Li, Guanying Huo, et al. · 2017 · Computational Intelligence and Neuroscience · 50 citations

    As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefo

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  21. A Survey on Image Classification and Activity Recognition using Deep Convolutional Neural Network Architecture

    M. Sornam, Kavitha Muthusubash, V. Vanitha · 2017 · International Conference on Advanced Computing · 47 citations

    Deep learning, over a decade it becomes the booming field for researchers since the technique has the capability to overcome the drawbacks of already used traditional algorithms which is dependent on hand designed features. Currently four different type of architecture used in deep learning which is an Autoencoder, Deep Belief Network, Convolutional Neural Network and Restricted Boltzmann Machine. According to the reported research, Convolutional Neural Network is very efficient on image classification and speech recognition. The main aim of this survey is to broadly cover the applications of convolutional networks in the field of computer-aided diagnosis for the dreadful diseases and also i

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  24. Image recognition using convolutional neural networks for classification of honey bee subspecies

    Dario De Nart, Cecília Costa, Gennaro Di Prisco, et al. · 2022 · Apidologie · 35 citations

    Abstract Four models based on convolutional neural networks were used to investigate whether image recognition techniques applied to honey bee wings could be used to discriminate among honey bee subspecies. A dataset consisting of 9887 wing images belonging to 7 subspecies and one hybrid was analysed with ResNet 50, MobileNet V2, Inception Net V3, and Inception ResNet V2. Accuracy values of classification of individual wings were over 0.92, and all models outperformed traditional morphometric evaluation. The Inception models achieved the highest accuracies and higher scores of precision and recall for most classes. When wing images were grouped by colony, almost all wings in the colony sampl

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