FedISM: Enhancing Data Imbalance via Shared Model in Federated Learning

Considering the sensitivity of data in medical scenarios, federated learning (FL) is suitable for applications that require data privacy. Medical personnel can use the FL framework for machine learning to assist in analyzing large-scale data that are protected within the institution. However, not all clients have the same distribution of datasets, so data imbalance problems occur among clients. The main challenge is to overcome the performance degradation caused by low accuracy and the inability to converge the model. This paper proposes a FedISM method to enhance performance in the case of Non-Independent Identically Distribution (Non-IID). FedISM exploits a shared model trained on a candidate dataset before performing FL among clients. The Candidate Selection Mechanism (CSM) was proposed to effectively select the most suitable candidate among clients for training the shared model. Based on the proposed approaches, FedISM not only trains the shared model without sharing any raw data, but it also provides an optimal solution through the selection of the best shared model. To evaluate performance, the proposed FedISM was applied to classify coronavirus disease (COVID), pneumonia, normal, and viral pneumonia in the experiments. The Dirichlet process was also used to simulate a variety of imbalanced data distributions. Experimental results show that FedISM improves accuracy by up to 25% when privacy concerns regarding patient data are rising among medical institutions.

HANDLING SEVERE DATA IMBALANCE IN CHEST X-RAY IMAGE CLASSIFICATION WITH TRANSFER LEARNING USING SWAV SELF-SUPERVISED PRE-TRAINING

Ever since the COVID-19 outbreak, numerous researchers have attempted to train accurate Deep Learning (DL) models, especially Convolutional Neural Networks (CNN), to assist medical personnel in diagnosing COVID-19 infections from Chest X-Ray (CXR) images. However, data imbalance and small dataset sizes have been an issue in training DL models for medical image classification tasks. On the other hand, most researchers focused on complex novel methods instead and few explored this problem. In this research, we demonstrated how Self-Supervised Learning (SSL) can assist DL models during pre-training, and Transfer Learning (TL) can be used in training the models, which can produce models that are more robust to data imbalance. The Swapping Assignment between Views (SwAV) algorithm in particular has been known to be outstanding in enhancing the accuracy of CNN models for classification tasks after TL. By training a ResNet-50 model pre-trained using SwAV on a severely imbalanced CXR dataset, the model managed to greatly outperform its counterpart pre-trained in a standard supervised manner. The SwAV-TL ResNet-50 model attained 0.952 AUROC with 0.821 macro-averaged F1 score when trained on the imbalanced dataset. Hence, it was proven that TL using models pre-trained through SwAV can achieve better accuracy even when the dataset is severely imbalanced, which is usually the case in medical image datasets. © 2023, SCIK Publishing Corporation. All rights reserved.

Medical Image Augmentation Framework for Resolving Chest X-Ray Data Imbalance

Deep learning techniques, particularly convolutional neural networks (CNNs), have led to an enormous breakthrough in the field of medical imaging. Since the onset of the COVID-19 pandemic, studies based on deep learning systems have shown excellent results for diagnosis through the use of Chest X-rays. However, these methods are data sensitive, and their effectiveness depends on the availability and reliability of data. Models trained on a class-imbalanced dataset tend to be biased towards the majority class. The class-imbalanced datasets can be balanced by augmenting them with synthetically generated images. This paper proposes a method for generating synthetic COVID-19 Chest X-Rays images using Generative Adversarial Networks (GANs). The images generated using the proposed GAN were augmented to three imbalanced datasets of real images. It was observed that the performance of the CNN model for COVID-19 classification improved with the augmented images. Significant improvement was seen in the sensitivity or recall, which is a very critical metric. The sensitivity achieved by adding GAN-generated synthetic images to each of the imbalanced datasets matched the sensitivity levels of the balanced dataset. Hence, the proposed solution can be used to generate images that boost the sensitivity of COVID-19 diagnosis to the level of a balanced dataset. Furthermore, this approach of synthetic data augmentation can be used in other medical classification applications for improved diagnosis recommendations. © 2022 University of Bahrain. All rights reserved.

VISAL-A novel learning strategy to address class imbalance.

In the imbalance data scenarios, Deep Neural Networks (DNNs) fail to generalize well on minority classes. In this letter, we propose a simple and effective learning function i.e, Visually Interpretable Space Adjustment Learning (VISAL) to handle the imbalanced data classification task. VISAL's objective is to create more room for the generalization of minority class samples by bringing in both the angular and euclidean margins into the cross-entropy learning strategy. When evaluated on the imbalanced versions of CIFAR, Tiny ImageNet, COVIDx and IMDB reviews datasets, our proposed method outperforms the state of the art works by a significant margin.

Credit Card Fraud Detection: Challenges and Techniques

Credit card usage has risen dramatically as a result of rapid advancements in electronic commerce and the unexpected circumstance of COVID. With credit cards becoming the most popular payment method for both offline and online transactions, the number of cases of fraud associated with them is rapidly increasing. In case of online fraud, it is not necessary for the perpetrators to be present at the scene of crime. The fraudulent activities can be accomplished by them in the seclusion of their homes through a multitude of methods for disguising their identities. VPNs are one way to obscure one's identity, as is routing communication through any Tor network for the victim, making it difficult to track back the culprit. © 2022 IEEE.

Road crash risk prediction during COVID-19 for flash crowd traffic prevention: The case of Los Angeles.

Road crashes are a major problem for traffic safety management, which usually causes flash crowd traffic with a profound influence on traffic management and communication systems. In 2020, the sudden outbreak of the novel coronavirus disease (COVID-19) pandemic led to significant changes in road traffic conditions. In this paper, by analyzing crash data from 2016 to 2020 and new COVID-19 case data in 2020, we find that the average crash severity and crash deaths during this period (a rapid increase of new COVID-19 cases in 2020) are higher than those in previous four years. Hence, it is necessary to exploit a novel road crash risk prediction model for such an emergency. We propose a novel data-adaptive fatigue focal loss (DA-FFL) method by fusing fatigue factors to establish a road crash risk prediction model under the scenario of large-scale emergencies. Finally, the experimental results demonstrate that DA-FFL performs better than the other typical methods in terms of area under curve (AUC) and false alarm rate (FAR) for imbalanced data. Furthermore, DA-FFL has better prediction performance in convolutional neural networks-long short-term memory (CNN-LSTM).

Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss.

Healthcare systems worldwide have been struggling since the beginning of the COVID-19 pandemic. The early diagnosis of this unprecedented infection has become their ultimate objective. Detecting positive patients from chest X-ray images is a quick and efficient solution for overloaded hospitals. Many studies based on deep learning (DL) techniques have shown high performance in classifying COVID-19 chest X-ray images. However, most of these studies suffer from a class imbalance problem mainly due to the limited number of COVID-19 samples. Such a problem may significantly reduce the efficiency of DL classifiers. In this work, we aim to build an accurate model that assists clinicians in the early diagnosis of COVID-19 using balanced data. To this end, we trained six state-of-the-art convolutional neural networks (CNNs) via transfer learning (TL) on three different COVID-19 datasets. The models were developed to perform a multi-classification task that distinguishes between COVID-19, normal, and viral pneumonia cases. To address the class imbalance issue, we first investigated the Weighted Categorical Loss (WCL) and then the Synthetic Minority Oversampling Technique (SMOTE) on each dataset separately. After a comparative study of the obtained results, we selected the model that achieved high classification results in terms of accuracy, sensitivity, specificity, precision, F1 score, and AUC compared to other recent works. DenseNet201 and VGG-19 claimed the best scores. With an accuracy of 98.87%, an F1_Score of 98.21%, a sensitivity of 98.86%, a specificity of 99.43%, a precision of 100%, and an AUC of 99.15%, the WCL combined with CheXNet outperformed the other examined models.

COVID-19 Article Classification Using Word-Embedding and Extreme Learning Machine with Various Kernels

The impact of the COVID-19 pandemic on the socially networked world cannot be understated. Entire industries need the latest information from across the globe at the earliest possible. The business world needs to cope with a very volatile market due to the pandemic. Businesses need to be swift in sensing potential profit opportunities and be updated on the changing consumer demands. Technological advances and medical procedures that successfully deal with COVID-19 can help save lives on the other side of the world. This seamless passage of crucial information, now more than ever, is only possible through the networked world. There are on average 821 articles published online on COVID-19 a day. Manually going through around 800 articles in a day is not feasible and highly time-consuming. This can prevent the industries and businesses from getting to the relevant information in time. We can optimize this task by applying machine learning techniques. In this work, six different word embedding techniques have been applied to the title and content of the articles to get an n-dimensional vector. These vectors are inputs for article classification models that employ Extreme Learning Machine (ELM) with linear, sigmoid, polynomial, and radial basis function kernels to train these models. We have also used feature selection techniques like the Analysis of Variance (ANOVA) test and Principal Component Analysis (PCA) to optimize the models. These models help to filter out relevant articles and speed up the process of getting crucial information to stay ahead of the competition and be the first to exploit new market opportunities. The experimental results highlight that the usage of word embedding techniques, feature selection techniques, and different ELM kernels help improve the accuracy of article classification. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images.

A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model's accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients.

Deep supervised learning using self-adaptive auxiliary loss for COVID-19 diagnosis from imbalanced CT images.

The outbreak and rapid spread of coronavirus disease 2019 (COVID-19) has had a huge impact on the lives and safety of people around the world. Chest CT is considered an effective tool for the diagnosis and follow-up of COVID-19. For faster examination, automatic COVID-19 diagnostic techniques using deep learning on CT images have received increasing attention. However, the number and category of existing datasets for COVID-19 diagnosis that can be used for training are limited, and the number of initial COVID-19 samples is much smaller than the normal's, which leads to the problem of class imbalance. It makes the classification algorithms difficult to learn the discriminative boundaries since the data of some classes are rich while others are scarce. Therefore, training robust deep neural networks with imbalanced data is a fundamental challenging but important task in the diagnosis of COVID-19. In this paper, we create a challenging clinical dataset (named COVID19-Diag) with category diversity and propose a novel imbalanced data classification method using deep supervised learning with a self-adaptive auxiliary loss (DSN-SAAL) for COVID-19 diagnosis. The loss function considers both the effects of data overlap between CT slices and possible noisy labels in clinical datasets on a multi-scale, deep supervised network framework by integrating the effective number of samples and a weighting regularization item. The learning process jointly and automatically optimizes all parameters over the deep supervised network, making our model generally applicable to a wide range of datasets. Extensive experiments are conducted on COVID19-Diag and three public COVID-19 diagnosis datasets. The results show that our DSN-SAAL outperforms the state-of-the-art methods and is effective for the diagnosis of COVID-19 in varying degrees of data imbalance.

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.