Rock classification model based on transfer learning and convolutional neural network

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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Fruit Classification Model Based on Improved Darknet53 Convolutional Neural Network

2020 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) ◽

10.1109/icitbs49701.2020.00194 ◽

2020 ◽

Author(s):

Hui Wang ◽

Fan Zhang ◽

Li Wang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Classification Model ◽

Model Based

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Pepper Disease Detection Model Based on Convolutional Neural Network and Transfer Learning

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/792/1/012001 ◽

2021 ◽

Vol 792 (1) ◽

pp. 012001

Author(s):

Yuting Zeng ◽

Yanbin Zhao ◽

YingHao Yu ◽

Yuan Tang ◽

Youwan Tang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Disease Detection ◽

Detection Model ◽

Model Based

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Multiple Music Sentiment Classification Model Based on Convolutional Neural Network

Communications in Computer and Information Science - Data Science ◽

10.1007/978-981-15-0118-0_48 ◽

2019 ◽

pp. 621-632

Author(s):

Jing Yang ◽

Fanfu Zeng ◽

Yong Wang ◽

Hairui Yu ◽

Le Zhang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Sentiment Classification ◽

Classification Model ◽

Model Based

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Detection of Epileptic Seizure Using Pretrained Deep Convolutional Neural Network and Transfer Learning

European Neurology ◽

10.1159/000512985 ◽

2020 ◽

Vol 83 (6) ◽

pp. 602-614

Author(s):

Hidir Selcuk Nogay ◽

Hojjat Adeli

Keyword(s):

Neural Network ◽

Machine Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Epileptic Seizure ◽

Binary Classification ◽

Classification Model ◽

Automatic Identification ◽

Eeg Signal ◽

Data Set

Introduction: The diagnosis of epilepsy takes a certain process, depending entirely on the attending physician. However, the human factor may cause erroneous diagnosis in the analysis of the EEG signal. In the past 2 decades, many advanced signal processing and machine learning methods have been developed for the detection of epileptic seizures. However, many of these methods require large data sets and complex operations. Methods: In this study, an end-to-end machine learning model is presented for detection of epileptic seizure using the pretrained deep two-dimensional convolutional neural network (CNN) and the concept of transfer learning. The EEG signal is converted directly into visual data with a spectrogram and used directly as input data. Results: The authors analyzed the results of the training of the proposed pretrained AlexNet CNN model. Both binary and ternary classifications were performed without any extra procedure such as feature extraction. By performing data set creation from short-term spectrogram graphic images, the authors were able to achieve 100% accuracy for binary classification for epileptic seizure detection and 100% for ternary classification. Discussion/Conclusion: The proposed automatic identification and classification model can help in the early diagnosis of epilepsy, thus providing the opportunity for effective early treatment.

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A Wafer Map Defect Pattern Classification Model Based on Deep Convolutional Neural Network

2020 IEEE 15th International Conference on Solid-State & Integrated Circuit Technology (ICSICT) ◽

10.1109/icsict49897.2020.9278021 ◽

2020 ◽

Author(s):

Dong-Yang Du ◽

Zheng Shi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Pattern Classification ◽

Deep Convolutional Neural Network ◽

Classification Model ◽

Model Based ◽

Defect Pattern

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

Procedia of Engineering and Life Science ◽

10.21070/pels.v1i1.820 ◽

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%.

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