An application of pre-trained CNN for image classification

Abdullah, .; Hasan, Mohammad S.

doi:10.1109/iccitechn.2017.8281779

Cited by 29 publications

(13 citation statements)

References 9 publications

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“…Then again, the customary A.I. classification algorithms in the new works are generally utilized for the presentation correlation with deep learning model, by and large, to represent the benefits of deep learning [15,16]. However, from our perspective, the impediments are the low-level highlights, not the classification algorithms.…”

Section: Related Workmentioning

confidence: 99%

SVM and KNN Based CNN Architectures for Plant Classification

Ghosh¹,

Singh²,

Kavita³

et al. 2022

Computers, Materials &Amp; Continua

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Automatic plant classification through plant leaf is a classical problem in Computer Vision. Plants classification is challenging due to the introduction of new species with a similar pattern and look-a-like. Many efforts are made to automate plant classification using plant leaf, plant flower, bark, or stem. After much effort, it has been proven that leaf is the most reliable source for plant classification. But it is challenging to identify a plant with the help of leaf structure because plant leaf shows similarity in morphological variations, like sizes, textures, shapes, and venation. Therefore, it is required to normalize all plant leaves into the same size to get better performance. Convolutional Neural Networks (CNN) provides a fair amount of accuracy when leaves are classified using this approach. But the performance can be improved by classifying using the traditional approach after applying CNN. In this paper, two approaches, namely CNN + Support Vector Machine (SVM) and CNN + K-Nearest Neighbors (kNN) used on 3 datasets, namely LeafSnap dataset, Flavia Dataset, and MalayaKew Dataset. The datasets are augmented to take care all the possibilities. The assessments and correlations of the predetermined feature extractor models are given. CNN + kNN managed to reach maximum accuracy of 99.5%, 97.4%, and 80.04%, respectively, in the three datasets.

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Section: Related Workmentioning

confidence: 99%

SVM and KNN Based CNN Architectures for Plant Classification

Ghosh¹,

Singh²,

Kavita³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…One research direction is to analyse the accurate number of objects which are occluded by the other objects in the image. The previous researches were based on visual attributes and trained CNN and computer vision [88], [89]. The occluded objects are not apparent visually in the image.…”

Section: Challengesmentioning

confidence: 99%

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

Yang¹,

Li²,

Rasul³

2021

IJMLC

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This paper reviewed recent literature on inventory management technologies and Artificial Intelligence (AI) applications. The classical Artificial Neural Network (ANN) models and computer vision technology applications for object classification were reviewed in particularly. The challenges of AI technologies in industrial warehouse management, particularly the ANN for solving object classification and counting are discussed. Some researchers reported the use of face recognition, moving vehicle classification and counting, which are easy to recognise objects on the floor or the ground. Other researchers explored the object counting technologies which are used to identify the visible objects on the ground or in images. Although several studies focused on industrial component identification and counting problems, a study on the warehouse receiving stage remains a blank canvas. This paper reviews and analyses current industrial warehouse management developments around AI applications in this field, which may provide a reference for future researchers and end-users for the best modelling approach to this specific problem at the warehouse receiving stage.

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“…Specifically, the Inception-Resnet_v2 has an image input shape as 299 x 299 x 3. After those models extracted the features from the images, then the user-defined layers were used for classifying the targets [11].…”

Section: Pretrained Cnnmentioning

confidence: 99%

Finding the Best Performing Pre-Trained CNN Model for Image Classification: Using a Class Activation Map to Spot Abnormal Parts in Diabetic Retinopathy Image

Jihyung¹

2021

AJBLS

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Diabetic retinopathy (DR) is a common eye disease that people get from diabetes. About 33.7% of the people with diabetes have DR. With our datas, which are pictures of the eyeball with and without DR, we tried different convolutional neural network (CNN) models to get the best accuracy score. We tested our datas with a default CNN model, and 5 different pre-trained models: MobileNet, VGG16, VGG19, Inception V3, and Inception ResNet V2. The default CNN model didn't perform very well, getting only 10.4%. The pre-trained model also didn't perform as good as expected, so we decided to use GRU with the models, which increases the score. For the higher accuracy, we added bidirectional GRU to train the whole parameters in the model. The 5 different pre-trained models scored an average of 74.2% accuracy score, and Inception ResNet V2 with bidirectional GRU included scored the highest accuracy, achieving 83.57%. For additional study, we used a class activation map to spot the abnormal parts of the eyeball with DR, and we could spot abnormal veins and bleeding on the eyeball. However, our research has limitations on that we did not use the segmentation methods, which is more advanced technique compared to classification, such as U-net, Fully Convolutional Network (FCN), Deep Lab V3, and Feature Pyramid Network. Furthermore, even though our model classified 5 different classes, the fact that the highest accuracy score was lower than 90% is also a limitation. For further study, we would prepare a masking method for applying segmentation methods to our dataset.

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An application of pre-trained CNN for image classification

Cited by 29 publications

References 9 publications

SVM and KNN Based CNN Architectures for Plant Classification

SVM and KNN Based CNN Architectures for Plant Classification

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

Finding the Best Performing Pre-Trained CNN Model for Image Classification: Using a Class Activation Map to Spot Abnormal Parts in Diabetic Retinopathy Image

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