A Comparative Study of Transfer Learning and Fine-Tuning Method on Deep Learning Models for Wayang Dataset Classification

Deep Learning is an essential technique in the classification problem in machine learning based on artificial neural networks. The general issue in deep learning is data-hungry, which require a plethora of data to train some model. Wayang is a shadow puppet art theater from Indonesia, especially in the Javanese culture. It has several indistinguishable characters. In this paper, We tried proposing some steps and techniques on how to classify the characters and handle the issue on a small wayang dataset by using model selection, transfer learning, and fine-tuning to obtain efficient and precise accuracy on our classification problem. The research used 50 images for each class and a total of 24 wayang characters classes. We collected and implemented various architectures from the initial version of deep learning to the latest proposed model and their state-of-art. The transfer learning and fine-tuning method showed a significant increase in accuracy, validation accuracy. By using Transfer Learning, it was possible to design the deep learning model with good classifiers within a short number of times on a small dataset. It performed 100% on their training on both EfficientNetB0 and MobileNetV3-small. On validation accuracy, gave 98.33% and 98.75%, respectively.

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An Improved Deep Learning Model for Traffic Crash Prediction

Journal of Advanced Transportation ◽

10.1155/2018/3869106 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13 ◽

Cited By ~ 14

Author(s):

Chunjiao Dong ◽

Chunfu Shao ◽

Juan Li ◽

Zhihua Xiong

Keyword(s):

Deep Learning ◽

Feature Learning ◽

Learning Model ◽

Fine Tuning ◽

Crash Prediction ◽

Traffic Crash ◽

Explanatory Variables ◽

Feature Representations ◽

Proposed Model ◽

Deep Learning Model

Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction. To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer. The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances. The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions. The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively.

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Towards detection and classification of microscopic foraminifera using transfer learning

Proceedings of the Northern Lights Deep Learning Workshop ◽

10.7557/18.5144 ◽

2020 ◽

Vol 1 ◽

pp. 6

Author(s):

Thomas Haugland Johansen ◽

Steffen Aagaard Sørensen

Keyword(s):

Deep Learning ◽

Life Cycle ◽

Transfer Learning ◽

Marine Sediments ◽

Barents Sea ◽

The Barents Sea ◽

Proposed Model ◽

Image Dataset ◽

Deep Learning Model

Foraminifera are single-celled marine organisms, which may have a planktic or benthic lifestyle. During their life cycle they construct shells consisting of one or more chambers, and these shells remain as fossils in marine sediments. Classifying and counting these fossils have become an important tool in e.g. oceanography and climatology.Currently the process of identifying and counting microfossils is performed manually using a microscope and is very time consuming. Developing methods to automate this process is therefore considered important across a range of research fields.The first steps towards developing a deep learning model that can detect and classify microscopic foraminifera are proposed. The proposed model is based on a VGG16 model that has been pretrained on the ImageNet dataset, and adapted to the foraminifera task using transfer learning. Additionally, a novel image dataset consisting of microscopic foraminifera and sediments from the Barents Sea region is introduced.

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v1 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract In the year 2020 world came to a halt due to spread of Covid-19 or SARS-CoV2 which was first identified in Wuhan, China. Since then, it has caused plethora of problems around the globe such as loss of millions of lives, economic instability etc. Less effectiveness of detection through Reverse Transcription Polymerase Chain Reaction and also prolonged time needed for detection through the same calls for a substitute for Covid-19 detection. Hence, in this study, we aim to develop a transfer learning based multi-class classifier using Chest X-Ray images which will classify the X-Ray images in 3 classes (Covid-19, Pneumonia, Normal). Further, the proposed model has been trained with deep learning classifiers namely: DenseNet201, Xception, ResNet50V2, VGG16, VGG-19, InceptionResNetV2 .These are evaluated on the basis of accuracy, precision and recall as performance parameters. It has been observed that DenseNet201 is the best deep learning model with 82.2% accuracy.

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Transfer Learning Based Multiclass Classification For Covid-19 Detection Using Chest X-Rays

10.21203/rs.3.rs-810768/v2 ◽

2021 ◽

Author(s):

Japman Singh Monga ◽

Yuvraj Singh Champawat ◽

Seema Kharb

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

X Rays ◽

Performance Parameters ◽

Chain Reaction ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

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A Deep Learning Model with Self-Supervised Learning and Attention Mechanism for COVID-19 Diagnosis Using Chest X-ray Images

Electronics ◽

10.3390/electronics10161996 ◽

2021 ◽

Vol 10 (16) ◽

pp. 1996

Author(s):

Junghoon Park ◽

Il-Youp Kwak ◽

Changwon Lim

Keyword(s):

Deep Learning ◽

Supervised Learning ◽

Learning Model ◽

Fine Tuning ◽

Classification Model ◽

World Health ◽

X Ray ◽

Proposed Model ◽

Chest X Ray ◽

Deep Learning Model

The SARS-CoV-2 virus has spread worldwide, and the World Health Organization has declared COVID-19 pandemic, proclaiming that the entire world must overcome it together. The chest X-ray and computed tomography datasets of individuals with COVID-19 remain limited, which can cause lower performance of deep learning model. In this study, we developed a model for the diagnosis of COVID-19 by solving the classification problem using a self-supervised learning technique with a convolution attention module. Self-supervised learning using a U-shaped convolutional neural network model combined with a convolution block attention module (CBAM) using over 100,000 chest X-Ray images with structure similarity (SSIM) index captures image representations extremely well. The system we proposed consists of fine-tuning the weights of the encoder after a self-supervised learning pretext task, interpreting the chest X-ray representation in the encoder using convolutional layers, and diagnosing the chest X-ray image as the classification model. Additionally, considering the CBAM further improves the averaged accuracy of 98.6%, thereby outperforming the baseline model (97.8%) by 0.8%. The proposed model classifies the three classes of normal, pneumonia, and COVID-19 extremely accurately, along with other metrics such as specificity and sensitivity that are similar to accuracy. The average area under the curve (AUC) is 0.994 in the COVID-19 class, indicating that our proposed model exhibits outstanding classification performance.

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Transfer Learning of a Deep Learning Model for Exploring Tourists’ Urban Image Using Geotagged Photos

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030137 ◽

2021 ◽

Vol 10 (3) ◽

pp. 137

Author(s):

Youngok Kang ◽

Nahye Cho ◽

Jiyoung Yoon ◽

Soyeon Park ◽

Jiyeon Kim

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Field Studies ◽

Learning Model ◽

Learning Technologies ◽

Regional Study ◽

Processing Technologies ◽

Final Model ◽

Urban Image ◽

Deep Learning Model

Recently, as computer vision and image processing technologies have rapidly advanced in the artificial intelligence (AI) field, deep learning technologies have been applied in the field of urban and regional study through transfer learning. In the tourism field, studies are emerging to analyze the tourists’ urban image by identifying the visual content of photos. However, previous studies have limitations in properly reflecting unique landscape, cultural characteristics, and traditional elements of the region that are prominent in tourism. With the purpose of going beyond these limitations of previous studies, we crawled 168,216 Flickr photos, created 75 scenes and 13 categories as a tourist’ photo classification by analyzing the characteristics of photos posted by tourists and developed a deep learning model by continuously re-training the Inception-v3 model. The final model shows high accuracy of 85.77% for the Top 1 and 95.69% for the Top 5. The final model was applied to the entire dataset to analyze the regions of attraction and the tourists’ urban image in Seoul. We found that tourists feel attracted to Seoul where the modern features such as skyscrapers and uniquely designed architectures and traditional features such as palaces and cultural elements are mixed together in the city. This work demonstrates a tourist photo classification suitable for local characteristics and the process of re-training a deep learning model to effectively classify a large volume of tourists’ photos.

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Transfer Learning of The ResNet-18 and DenseNet-121 Model Used to Diagnose Intracranial Hemorrhage in CT Scanning

Current Pharmaceutical Design ◽

10.2174/1381612827666211213143357 ◽

2021 ◽

Vol 27 ◽

Author(s):

Qi Zhou ◽

Wenjie Zhu ◽

Fuchen Li ◽

Mingqing Yuan ◽

Linfeng Zheng ◽

...

Keyword(s):

Deep Learning ◽

Transfer Learning ◽

Intracranial Hemorrhage ◽

Intraventricular Hemorrhage ◽

Learning Model ◽

Normal Group ◽

Subdural Hemorrhage ◽

Model Assessment ◽

Learning Models ◽

Deep Learning Model

Objective: To verify the ability of the deep learning model in identifying five subtypes and normal images in noncontrast enhancement CT of intracranial hemorrhage. Method: A total of 351 patients (39 patients in the normal group, 312 patients in the intracranial hemorrhage group) performed with intracranial hemorrhage noncontrast enhanced CT were selected, with 2768 images in total (514 images for the normal group, 398 images for the epidural hemorrhage group, 501 images for the subdural hemorrhage group, 497 images for the intraventricular hemorrhage group, 415 images for the cerebral parenchymal hemorrhage group, and 443 images for the subarachnoid hemorrhage group). Based on the diagnostic reports of two radiologists with more than 10 years of experience, the ResNet-18 and DenseNet-121 deep learning models were selected. Transfer learning was used. 80% of the data was used for training models, 10% was used for validating model performance against overfitting, and the last 10% was used for the final evaluation of the model. Assessment indicators included accuracy, sensitivity, specificity, and AUC values. Results: The overall accuracy of ResNet-18 and DenseNet-121 models were 89.64% and 82.5%, respectively. The sensitivity and specificity of identifying five subtypes and normal images were above 0.80. The sensitivity of DenseNet-121 model to recognize intraventricular hemorrhage and cerebral parenchymal hemorrhage was lower than 0.80, 0.73, and 0.76 respectively. The AUC values of the two deep learning models were above 0.9. Conclusion: The deep learning model can accurately identify the five subtypes of intracranial hemorrhage and normal images, and it can be used as a new tool for clinical diagnosis in the future.

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Deep Learning-Based Water Crystal Classification

Applied Sciences ◽

10.3390/app12020825 ◽

2022 ◽

Vol 12 (2) ◽

pp. 825

Author(s):

Hien Doan Thi ◽

Frederic Andres ◽

Long Tran Quoc ◽

Hiro Emoto ◽

Michiko Hayashi ◽

...

Keyword(s):

Deep Learning ◽

Water Resources ◽

Past Research ◽

Fine Tuning ◽

Quality Of Water ◽

Snow Crystal ◽

State Of Water ◽

First World ◽

Small Dataset

Much of the earth’s surface is covered by water. As was pointed out in the 2020 edition of the World Water Development Report, climate change challenges the sustainability of global water resources, so it is important to monitor the quality of water to preserve sustainable water resources. Quality of water can be related to the structure of water crystal, the solid-state of water, so methods to understand water crystals can help to improve water quality. As a first step, a water crystal exploratory analysis has been initiated with the cooperation with the Emoto Peace Project (EPP). The 5K EPP dataset has been created as the first world-wide small dataset of water crystals. Our research focused on reducing the inherent limitations when fitting machine learning models to the 5K EPP dataset. One major result is the classification of water crystals and how to split our small dataset into several related groups. Using the 5K EPP dataset of human observations and past research on snow crystal classification, we created a simple set of visual labels to identify water crystal shapes, in 13 categories. A deep learning-based method has been used to automatically do the classification task with a subset of the label dataset. The classification achieved high accuracy when using a fine-tuning technique.

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An Enhanced Faster-RCNN Based Deep Learning Model for Crop Diseases Detection and Classification

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f9212.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4714-4719

Keyword(s):

Neural Network ◽

Vital Role ◽

Convolution Neural Network ◽

Plant Phenotyping ◽

Early Disease ◽

Early Disease Detection ◽

Proposed Model ◽

Crop Diseases ◽

Deep Learning Model ◽

State Of Art

Recently Plant phenotyping has gained the attention of many researchers such that it plays a vital role in the context of enhancing agricultural productivity. Indian economy highly depends on agriculture and this factor elevates the importance of early disease detection of the crops within the agricultural fields. Addressing this problem several researchers have proposed Computer Vision and Pattern recognition based mechanisms through which they have attempted to identify the infected crops in the early stages.in this scenario, CNN convolution neural network-based architecture has demonstrated exceptional performance when compared with state-of-art mechanisms. This paper introduces an enhanced RCNN recurrent convolution neural network-based architecture that enhances the prediction accuracy while detecting the crop diseases in early stages. Based on the simulative studies is observed that the proposed model outperforms when compared with CNN and other state-of-art mechanisms.

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Detecting Damage Building Using Real-Time Crowdsourced Images And Transfer Learning

10.21203/rs.3.rs-964756/v1 ◽

2021 ◽

Author(s):

Gaurav Chachra ◽

Qingkai Kong ◽

Jim Huang ◽

Srujay Korlakunta ◽

Jennifer Grannen ◽

...

Keyword(s):

Social Media ◽

Deep Learning ◽

Real Time ◽

Transfer Learning ◽

Learning Model ◽

Research Community ◽

Rescue Work ◽

The Public ◽

Social Media Platforms ◽

Deep Learning Model

Abstract After significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important locations on the images that facilitate the decision.

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