Transfer learning for image classification using VGG19: Caltech-101 image data set

<p>The acknowledgment of plant diseases assumes an indispensable part in taking infectious prevention measures to improve the quality and amount of harvest yield. Mechanization of plant diseases is a lot advantageous as it decreases the checking work in an enormous cultivated area where mango is planted to a huge extend. Leaves being the food hotspot for plants, the early and precise recognition of leaf diseases is significant. This work focused on grouping and distinguishing the diseases of mango leaves through the process of CNN. DenseNet201, InceptionResNetV2, InceptionV3, ResNet50, ResNet152V2, and Xception all these models of CNN with transfer learning techniques are used here for getting better accuracy from the targeted data set. Image acquisition, image segmentation, and features extraction are the steps involved in disease detection. Different kinds of leaf diseases which are considered as the class for this work such as anthracnose, gall machi, powdery mildew, red rust are used in the dataset consisting of 1500 images of diseased and also healthy mango leaves image data another class is also added in the dataset. We have also evaluated the overall performance matrices and found that the DenseNet201 outperforms by obtaining the highest accuracy as 98.00% than other models.</p>

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Comparison of Different Image Data Augmentation Approaches

Journal of Imaging ◽

10.3390/jimaging7120254 ◽

2021 ◽

Vol 7 (12) ◽

pp. 254

Author(s):

Loris Nanni ◽

Michelangelo Paci ◽

Sheryl Brahnam ◽

Alessandra Lumini

Keyword(s):

Image Classification ◽

Data Augmentation ◽

Image Data ◽

Research Literature ◽

Discrete Wavelet ◽

Data Sets ◽

Data Set ◽

Additional Information ◽

Comparable Performance ◽

Data Points

Convolutional neural networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the discrete wavelet transform and the other on the constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across four benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, a portrait dataset, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art (or comparable) performance across all four data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification.

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Cross-Modality Transfer Learning for Image-Text Information Management

ACM Transactions on Management Information Systems ◽

10.1145/3464324 ◽

2022 ◽

Vol 13 (1) ◽

pp. 1-14

Author(s):

Shuteng Niu ◽

Yushan Jiang ◽

Bowen Chen ◽

Jian Wang ◽

Yongxin Liu ◽

...

Keyword(s):

Image Classification ◽

Information Management ◽

Transfer Learning ◽

Information Transfer ◽

Image Data ◽

Machine Learning Algorithms ◽

Training Data ◽

Classification Problems ◽

Target Domain ◽

Text Data

In the past decades, information from all kinds of data has been on a rapid increase. With state-of-the-art performance, machine learning algorithms have been beneficial for information management. However, insufficient supervised training data is still an adversity in many real-world applications. Therefore, transfer learning (TF) was proposed to address this issue. This article studies a not well investigated but important TL problem termed cross-modality transfer learning (CMTL). This topic is closely related to distant domain transfer learning (DDTL) and negative transfer. In general, conventional TL disciplines assume that the source domain and the target domain are in the same modality. DDTL aims to make efficient transfers even when the domains or the tasks are entirely different. As an extension of DDTL, CMTL aims to make efficient transfers between two different data modalities, such as from image to text. As the main focus of this study, we aim to improve the performance of image classification by transferring knowledge from text data. Previously, a few CMTL algorithms were proposed to deal with image classification problems. However, most existing algorithms are very task specific, and they are unstable on convergence. There are four main contributions in this study. First, we propose a novel heterogeneous CMTL algorithm, which requires only a tiny set of unlabeled target data and labeled source data with associate text tags. Second, we introduce a latent semantic information extraction method to connect the information learned from the image data and the text data. Third, the proposed method can effectively handle the information transfer across different modalities (text-image). Fourth, we examined our algorithm on a public dataset, Office-31. It has achieved up to 5% higher classification accuracy than “non-transfer” algorithms and up to 9% higher than existing CMTL algorithms.

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Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

Sensors ◽

10.3390/s21175809 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5809

Author(s):

Loris Nanni ◽

Giovanni Minchio ◽

Sheryl Brahnam ◽

Davide Sarraggiotto ◽

Alessandra Lumini

Keyword(s):

Image Classification ◽

Image Data ◽

Support Vector ◽

Final Decision ◽

Data Sets ◽

Data Set ◽

Additional Information ◽

New Strategy ◽

Fully Connected ◽

Siamese Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN are closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

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Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens

PLoS ONE ◽

10.1371/journal.pone.0243243 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243243

Author(s):

Asim Khan ◽

Umair Nawaz ◽

Anwaar Ulhaq ◽

Randall W. Robinson

Keyword(s):

Real Time ◽

Transfer Learning ◽

Image Data ◽

Fruit Trees ◽

Disease Diagnosis ◽

Economic Losses ◽

Plant Leaves ◽

Data Set ◽

Plant Leaf

The control of plant leaf diseases is crucial as it affects the quality and production of plant species with an effect on the economy of any country. Automated identification and classification of plant leaf diseases is, therefore, essential for the reduction of economic losses and the conservation of specific species. Various Machine Learning (ML) models have previously been proposed to detect and identify plant leaf disease; however, they lack usability due to hardware sophistication, limited scalability and realistic use inefficiency. By implementing automatic detection and classification of leaf diseases in fruit trees (apple, grape, peach and strawberry) and vegetable plants (potato and tomato) through scalable transfer learning on Amazon Web Services (AWS) SageMaker and importing it into AWS DeepLens for real-time functional usability, our proposed DeepLens Classification and Detection Model (DCDM) addresses such limitations. Scalability and ubiquitous access to our approach is provided by cloud integration. Our experiments on an extensive image data set of healthy and unhealthy fruit trees and vegetable plant leaves showed 98.78% accuracy with a real-time diagnosis of diseases of plant leaves. To train DCDM deep learning model, we used forty thousand images and then evaluated it on ten thousand images. It takes an average of 0.349s to test an image for disease diagnosis and classification using AWS DeepLens, providing the consumer with disease information in less than a second.

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Closing the Performance Gap between Siamese Networks for Dissimilarity Image Classification and Convolutional Neural Networks

10.20944/preprints202108.0094.v1 ◽

2021 ◽

Author(s):

Loris Nanni ◽

Giovanni Minchio ◽

Sheryl Brahnam ◽

Davide Sarraggiotto ◽

Alessandra Lumini

Keyword(s):

Image Classification ◽

Image Data ◽

Support Vector ◽

Final Decision ◽

Data Sets ◽

Data Set ◽

Additional Information ◽

New Strategy ◽

Fully Connected ◽

Siamese Networks

In this paper, we examine two strategies for boosting the performance of ensembles of Siamese networks (SNNs) for image classification using two loss functions (Triplet and Binary Cross Entropy) and two methods for building the dissimilarity spaces (FULLY and DEEPER). With FULLY, the distance between a pattern and a prototype is calculated by comparing two images using the fully connected layer of the Siamese network. With DEEPER, each pattern is described using a deeper layer combined with dimensionality reduction. The basic design of the SNNs takes advantage of supervised k-means clustering for building the dissimilarity spaces that train a set of support vector machines, which are then combined by sum rule for a final decision. The robustness and versatility of this approach are demonstrated on several cross-domain image data sets, including a portrait data set, two bioimage and two animal vocalization data sets. Results show that the strategies employed in this work to increase the performance of dissimilarity image classification using SNN is closing the gap with standalone CNNs. Moreover, when our best system is combined with an ensemble of CNNs, the resulting performance is superior to an ensemble of CNNs, demonstrating that our new strategy is extracting additional information.

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Breast cancer X-ray image staging: based on efficient net with multi-scale fusion and cbam attention

Journal of Physics Conference Series ◽

10.1088/1742-6596/2082/1/012006 ◽

2021 ◽

Vol 2082 (1) ◽

pp. 012006

Author(s):

Runyi Li ◽

Sen Wang ◽

Zizhou Wang ◽

Lei Zhang

Keyword(s):

Breast Cancer ◽

Image Classification ◽

Medical Image ◽

Image Data ◽

Attention Mechanism ◽

Phase Iii ◽

Classification Algorithms ◽

Data Set ◽

Multi Scale ◽

Medical Image Data

Abstract With ever-progressing development period, image classification algorithms based on deep learning have shown good performance on some large datasets. In the development of classification algorithms, many proposals related to attention mechanism have greatly improved the accuracy of the model, and at the same time increased the interpretability of the network structure. However, on medical image data, the performance of the classification algorithm is not as expected, and the reason is that the fine-grained image data differs little among all classes, resulting that the knowledge domain is also hard to learn for models. We (1) proposed the Efficientnet model based on the cbam attention mechanism, and added a multi-scale fusion method; (2) applied the model to the breast cancer medical image data set, and completed the breast cancer classification task with high accuracy (Phase I, Phase II, Phase III, etc.); (3) Compared with other existing image classification algorithms, our method has the highest accuracy, thus the researchers conclude that EfficientNet with CBAM and multi-scale fusion will improve the classification performance. This result is helpful for deeper research on medical image processing and breast cancer staging.

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Application Research on the Medical imaging testing of Novel Coronavirus Pneumonia COVID-19 based on Transfer Learning

10.21203/rs.3.rs-771648/v1 ◽

2021 ◽

Author(s):

shouqiang Liu ◽

Mingyue Jiang ◽

Liming Chen ◽

Yang Wang

Keyword(s):

Medical Imaging ◽

Transfer Learning ◽

Image Data ◽

Training Model ◽

Small Sample ◽

Detection Methods ◽

Nucleic Acid Detection ◽

Small Data ◽

Data Set ◽

Novel Coronavirus

Abstract Novel coronavirus pneumonia (COVID-19) is a highly infectious and fatal pneumonia-type disease that poses a great threat to the public safety of society. A fast and efficient method for screening COVID19-positive patients is essential. At present, the main detection methods are nucleic acid detection of manual diagnosis and medical imaging (CT image/X-ray image), both of which take a long time to obtain the diagnosis result. This paper discusses the common processing methods for the problem of insufficient medical image data. Then, transfer learning and convolutional neural network were used to construct the screening and diagnosis model of COVID-19, and different migration models were analyzed and compared to select a better pre-training model, which was trained and analyzed under small data sets. Finally, it analyzes and discusses how to train a highly reliable model to quickly help doctors provide advice in the critical moment of epidemic prevention and control when only a small sample data set is available.

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Comparison of Different Image Data Augmentation Approaches

10.20944/preprints202111.0047.v1 ◽

2021 ◽

Author(s):

Loris Nanni ◽

Michelangelo Paci ◽

Sheryl Brahnam ◽

Alessandra Lumini

Keyword(s):

Image Classification ◽

Data Augmentation ◽

Image Data ◽

Research Literature ◽

Discrete Wavelet ◽

Data Sets ◽

Data Set ◽

Additional Information ◽

Classification Code ◽

Data Points

Convolutional Neural Networks (CNNs) have gained prominence in the research literature on image classification over the last decade. One shortcoming of CNNs, however, is their lack of generalizability and tendency to overfit when presented with small training sets. Augmentation directly confronts this problem by generating new data points providing additional information. In this paper, we investigate the performance of more than ten different sets of data augmentation methods, with two novel approaches proposed here: one based on the Discrete Wavelet Transform and the other on the Constant-Q Gabor transform. Pretrained ResNet50 networks are finetuned on each augmentation method. Combinations of these networks are evaluated and compared across three benchmark data sets of images representing diverse problems and collected by instruments that capture information at different scales: a virus data set, a bark data set, and a LIGO glitches data set. Experiments demonstrate the superiority of this approach. The best ensemble proposed in this work achieves state-of-the-art performance across all three data sets. This result shows that varying data augmentation is a feasible way for building an ensemble of classifiers for image classification (code available at https://github.com/LorisNanni).

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Deep Learning Approaches for Whiteboard Image Quality Enhancement

Color and Imaging Conference ◽

10.2352/j.imagingsci.technol.2019.63.4.040404 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 360-368

Author(s):

Mekides Assefa Abebe ◽

Jon Yngve Hardeberg

Keyword(s):

Deep Learning ◽

Image Quality ◽

Image Data ◽

Quality Enhancement ◽

Network Architectures ◽

Learning Approaches ◽

Data Set ◽

Image Quality Enhancement ◽

Processing Techniques ◽

White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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