Comparison of Deep Learning, Data Augmentation and Bag of-Visual-Words for Classification of Imbalanced Image Datasets

2019 ◽  
pp. 561-571 ◽  
Author(s):  
Manisha Saini ◽  
Seba Susan
Keyword(s):  
Deep Learning ◽  
Data Augmentation ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Learning Data ◽  
Image Datasets

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

Object Classification of Aerial Images With Bag-of-Visual Words

2010 ◽  
Vol 7 (2) ◽  
pp. 366-370 ◽  
Author(s):  
Sheng Xu ◽  
Tao Fang ◽  
Deren Li ◽  
Shiwei Wang
Keyword(s):  
Object Classification ◽  
Aerial Images ◽  
Bag Of Visual Words ◽  
Visual Words

scholarly journals Logo Classification and Data Augmentation Techniques for PCB Assurance and Counterfeit Detection

2021 ◽  
Author(s):  
Mukhil Azhagan Mallaiyan Sathiaseelan ◽  
Olivia P. Paradis ◽  
Rajat Rai ◽  
Suryaprakash Vasudev Pandurangi ◽  
Manoj Yasaswi Vutukuru ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Bag Of Visual Words ◽  
Visual Words ◽  
Bill Of Materials ◽  
Augmentation Techniques ◽  
Classification Tasks ◽  
Index Terms ◽  
Fully Connected ◽  
Future Work

Abstract In this manuscript, we present our work on Logo classification in PCBs for Hardware assurance purposes. Identifying and classifying logos have important uses for text detection, component authentication and counterfeit detection. Since PCB assurance faces the lack of a representative dataset for classification and detection tasks, we collect different variants of logos from PCBs and present data augmentation techniques to create the necessary data to perform machine learning. In addition to exploring the challenges for image classification tasks in PCBs, we present experiments using Random Forest classifiers, Bag of Visual Words (BoVW) using SIFT and ORB Fully Connected Neural Networks (FCN) and Convolutional Neural Network (CNN) architectures. We present results and also a discussion on the edge cases where our algorithms fail including the potential for future work in PCB logo detection. The code for the algorithms along with the dataset that includes 18 classes of logos with 14000+ images is provided at this link: https://www.trusthub.org/#/data Index Terms—AutoBoM, Logo classification, Data augmentation, Bill of materials, PCB Assurance, Hardware Assurance, Counterfeit avoidance

scholarly journals Acral melanoma detection using dermoscopic images and convolutional neural networks

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Qaiser Abbas ◽  
Farheen Ramzan ◽  
Muhammad Usman Ghani
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Transfer Learning ◽  
Clinical Decision Making ◽  
Data Augmentation ◽  
Automated System ◽  
Convolutional Network ◽  
Acral Melanoma ◽  
Melanoma Subtypes

AbstractAcral melanoma (AM) is a rare and lethal type of skin cancer. It can be diagnosed by expert dermatologists, using dermoscopic imaging. It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers. Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma. However, to date, limited research has been conducted on the classification of melanoma subtypes. The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes, such as, AM. In this study, we present a novel deep learning model, developed to classify skin cancer. We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions. Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection. Our custom-built model is a seven-layered deep convolutional network that was trained from scratch. Additionally, transfer learning was utilized to compare the performance of our model, where AlexNet and ResNet-18 were modified, fine-tuned, and trained on the same dataset. We achieved improved results from our proposed model with an accuracy of more than 90 % for AM and benign nevus, respectively. Additionally, using the transfer learning approach, we achieved an average accuracy of nearly 97 %, which is comparable to that of state-of-the-art methods. From our analysis and results, we found that our model performed well and was able to effectively classify skin cancer. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

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