A practical study of neural network-based image classification model trained with transfer learning method

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%.

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Transfer learning-based Fashion Image Classification using Hybrid 2D-CNN and ImageNet Neural Network

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39054 ◽

2021 ◽

Vol 9 (11) ◽

pp. 1537-1545

Author(s):

Sweety Duseja

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Classification ◽

Transfer Learning ◽

Learning Strategy ◽

Learning Transfer ◽

Learning Approach ◽

Learning Method ◽

Product Identification ◽

Classification Product

Abstract: Many algorithms have been developed as a result of recent advances in machine learning to handle a variety of challenges. In recent years, the most popular transfer learning method has allowed researchers and engineers to run experiments with minimal computing and time resources. To tackle the challenges of classification, product identification, product suggestion, and picture-based search, this research proposed a transfer learning strategy for Fashion image classification based on hybrid 2D-CNN pretrained by VGG-16 and AlexNet. Pre-processing, feature extraction, and classification are the three parts of the proposed system's implementation. We used the Fashion MNIST dataset, which consists of 50,000 fashion photos that have been classified. Training and validation datasets have been separated. In comparison to other conventional methodologies, the suggested transfer learning approach has higher training and validation accuracy and reduced loss. Keywords: Machine Learning, Transfer Learning, Convolutional Neural Network, Image Classification, VGG16, AlexNet, 2D CNN.

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Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

Scientific Reports ◽

10.1038/s41598-020-78129-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Young-Gon Kim ◽

Sungchul Kim ◽

Cristina Eunbee Cho ◽

In Hye Song ◽

Hee Jin Lee ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Frozen Section ◽

Medical Center ◽

External Validation ◽

Model Performance ◽

Classification Model ◽

Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

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An Xception Based Convolutional Neural Network for Scene Image Classification with Transfer Learning

2020 2nd International Conference on Information Technology and Computer Application (ITCA) ◽

10.1109/itca52113.2020.00063 ◽

2020 ◽

Author(s):

Xizhi Wu ◽

Rongzhe Liu ◽

Hanqing Yang ◽

Zizhao Chen

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Scene Image

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A Transfer Learning Method based on VGG-16 Convolutional Neural Network for MI Classification

10.1109/ccdc52312.2021.9602818 ◽

2021 ◽

Author(s):

Ming-Ai Li ◽

Dong-Qin Xu

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Learning Method

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Deep Convolution Neural Network with 2-Stage Transfer Learning for Medical Image Classification

The Brain & Neural Networks ◽

10.3902/jnns.24.3 ◽

2017 ◽

Vol 24 (1) ◽

pp. 3-12 ◽

Cited By ~ 1

Author(s):

Hayaru Shouno ◽

Aiga Suzuki ◽

Satoshi Suzuki ◽

Shoji Kido

Keyword(s):

Neural Network ◽

Image Classification ◽

Transfer Learning ◽

Medical Image ◽

Convolution Neural Network ◽

Deep Convolution Neural Network ◽

Medical Image Classification

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Image Classification Using Transfer Learning and Deep Learning

International Journal Of Engineering And Computer Science ◽

10.18535/ijecs/v10i9.4622 ◽

2021 ◽

Vol 10 (9) ◽

pp. 25394-25398

Author(s):

Chitra Desai

Keyword(s):

Neural Network ◽

Deep Learning ◽

Image Classification ◽

Transfer Learning ◽

Network Model ◽

Neural Network Model ◽

Large Scale ◽

Deep Neural Network ◽

Visual Recognition ◽

Classification Of Images

Deep learning models have demonstrated improved efficacy in image classification since the ImageNet Large Scale Visual Recognition Challenge started since 2010. Classification of images has further augmented in the field of computer vision with the dawn of transfer learning. To train a model on huge dataset demands huge computational resources and add a lot of cost to learning. Transfer learning allows to reduce on cost of learning and also help avoid reinventing the wheel. There are several pretrained models like VGG16, VGG19, ResNet50, Inceptionv3, EfficientNet etc which are widely used. This paper demonstrates image classification using pretrained deep neural network model VGG16 which is trained on images from ImageNet dataset. After obtaining the convolutional base model, a new deep neural network model is built on top of it for image classification based on fully connected network. This classifier will use features extracted from the convolutional base model.

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