An image classification model based on transfer learning for ulcerative proctitis

2021 ◽  
Author(s):  
Feng Zeng ◽  
Xingcun Li ◽  
Xiaoheng Deng ◽  
Lan Yao ◽  
Guanghui Lian
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Classification Model ◽  
Ulcerative Proctitis ◽  
Model Based

scholarly journals A Transfer Learning Strategy for Owl Sound Classification by Using Image Classification Model with Audio Spectrogram

2021 ◽  
Vol 13 (3) ◽  
pp. 546-553
Author(s):  
Kevin William Gunawan ◽  
◽  
Alam Ahmad Hidayat ◽  
Tjeng Wawan Cenggoro ◽  
Bens Pardamean ◽  
...  
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Learning Strategy ◽  
Classification Model ◽  
Sound Classification

scholarly journals A Semi-Supervised Image Classification Model Based on Improved Ensemble Projection Algorithm

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 1372-1379 ◽  
Author(s):  
Qiguang Miao ◽  
Ruyi Liu ◽  
Peipei Zhao ◽  
Yunan Li ◽  
Erqiang Sun
Keyword(s):  
Image Classification ◽  
Classification Model ◽  
Projection Algorithm ◽  
Model Based ◽  
Supervised Image Classification

scholarly journals Implementation of Transfer Learning in the Convolutional Neural Network Algorithm for Identification of Potato Leaf Disease

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Abdul Jalil Rozaqi ◽  
Muhammad Rudyanto Arief ◽  
Andi Sunyoto
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Method Development ◽  
Classification Model ◽  
Learning Method ◽  
Potato Leaf ◽  
Leaf Disease

Potatoes are a plant that has many benefits for human life. The potato plant has a problem, namely a disease that attacks the leaves. Disease on potato leaves that is often encountered is early blight and late blight. Image processing is a method that can be used to assist farmers in identifying potato leaf disease by utilizing leaf images. Image processing method development has been done a lot, one of which is by using the Convolutional Neural Network (CNN) algorithm. The CNN method is a good image classification algorithm because its layer architecture can extract leaf image features in depth, however, determining a good CNN architectural model requires a lot of data. CNN architecture will become overfitting if it uses less data, where the classification model has high accuracy on training data but the accuracy becomes poor on test data or new data. This research utilizes the Transfer Learning method to avoid an overfit model when the data used is not ideal or too little. Transfer Learning is a method that uses the CNN architecture that has been trained by other data previously which is then used for image classification on the new data. The purpose of this research was to use the Transfer Learning method on CNN architecture to classify potato leaf images in identifying potato leaf disease. This research compares the Transfer Learning method used to find the best method. The results of the experiments in this research indicate that the Transfer Learning VGG-16 method has the best classification performance results, this method produces the highest accuracy value of 95%.

scholarly journals Deep Transfer Learning based Fusion Model for Environmental Remote Sensing Image Classification Model

2022 ◽  
pp. 1-12
Author(s):  
Anwer Mustafa Hilal ◽  
Fahd N. Al-Wesabi ◽  
Khalid J Alzahrani​ ◽  
Mesfer Al Duhayyim ◽  
Manar Ahmed Hamza ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Classification ◽  
Transfer Learning ◽  
Remote Sensing Image ◽  
Classification Model ◽  
Fusion Model ◽  
Remote Sensing Image Classification ◽  
Environmental Remote Sensing

scholarly journals 4S-DT: Self Supervised Super Sample Decomposition for Transfer learning with application to COVID-19 detection

2020 ◽  
Author(s):  
Asmaa Abbas ◽  
Mohammed M. Abdelsamea ◽  
Mohamed Medhat Gaber
Keyword(s):  
Image Classification ◽  
Transfer Learning ◽  
Large Scale ◽  
Learning Strategy ◽  
Classification Model ◽  
Decomposition Approach ◽  
X Ray ◽  
Sample Decomposition ◽  
Chest X Ray ◽  
Coarse To Fine

ABSTRACTDue to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this paper, we propose a novel deep convolutional neural network, we called Self Supervised Super Sample Decomposition for Transfer learning (4S-DT) model. 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabelled chest X-ray images. 4S-DT helps in improving the robustness of knowledge transformation via a downstream learning strategy with a class-decomposition layer to simplify the local structure of the data. 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream class-decomposition mechanism. We used 50,000 unlabelled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. 4S-DT has achieved an accuracy of 97.54% in the detection of COVID-19 cases on an extended test set enriched by augmented images, out of which all real COVID-19 cases were detected, which was the highest accuracy obtained when compared to other methods.

scholarly journals News Video Classification Model Based on ResNet-2 and Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yiping Gao
Keyword(s):  
Transfer Learning ◽  
Adaptive Learning ◽  
Descent Method ◽  
Classification Model ◽  
Gradient Descent Method ◽  
Video Classification ◽  
Important Research ◽  
Neural Network Models ◽  
Model Based ◽  
News Video

A large amount of useful information is included in the news video, and how to classify the news video information has become an important research topic in the field of multimedia technology. News videos are enormously informative, and employing manual classification methods is too time-consuming and vulnerable to subjective judgment. Therefore, developing an automated news video analysis and retrieval method becomes one of the most important research contents in the current multimedia information system. Therefore, this paper proposes a news video classification model based on ResNet-2 and transfer learning. First, a model-based transfer method was adopted to transfer the commonality knowledge of the pretrained model of the Inception-ResNet-v2 network on ImageNet, and a news video classification model was constructed. Then, a momentum update rule is introduced on the basis of the Adam algorithm, and an improved gradient descent method is proposed in order to obtain an optimal solution of the local minima of the function in the learning process. The experimental results show that the improved Adam algorithm can iteratively update the network weights through the adaptive learning rate to reach the fastest convergence. Compared with other convolutional neural network models, the modified Inception-ResNet-v2 network model achieves 91.47% classification accuracy for common news video datasets.

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