Data Augmentation Techniques Research Articles

Development of an artificial intelligence model for predicting implant size in total knee arthroplasty using simple X-ray images

BackgroundAccurate estimation of implant size before surgery is crucial in preparing for total knee arthroplasty. However, this task is time-consuming and labor-intensive. To alleviate this burden on surgeons, we developed a reliable artificial intelligence (AI) model to predict implant size.MethodsWe enrolled 714 patients with knee osteoarthritis who underwent total knee arthroplasty from March 2010 to February 2014. All surgeries were performed by the same surgeon using implants from the same manufacturer. We collected 1412 knee anteroposterior (AP) and lateral view x-ray images and retrospectively investigated the implant size. We trained the AI model using both AP and lateral images without any clinical or demographic information and performed data augmentation to resolve issues of uneven distribution and insufficient data. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. Using data augmentation techniques, we generated 500 images for each size of the femur and tibia, which were then used to train the model. We used ResNet-101 and optimized the model with the aim of minimizing the cross-entropy loss function using both the Stochastic Gradient Descent (SGD) and Adam optimizer.ResultsThe SGD optimizer achieved the best performance in internal validation. The model showed micro F1-score 0.91 for femur and 0.87 for tibia. For predicting within ± one size, micro F1-score was 0.99 for femur and 0.98 for tibia.ConclusionWe developed a deep learning model with high predictive power for implant size using only simple x-ray images. This could help surgeons reduce the time and labor required for preoperative preparation in total knee arthroplasty. While similar studies have been conducted, our work is unique in its use of simple x-ray images without any other data, like demographic features, to achieve a model with strong predictive power.

An efficient brain tumor detection and classification using pre-trained convolutional neural network models

In cases of brain tumors, some brain cells experience abnormal and rapid growth, leading to the development of tumors. Brain tumors represent a significant source of illness affecting the brain. Magnetic Resonance Imaging (MRI) stands as a well-established and coherent diagnostic method for brain cancer detection. However, the resulting MRI scans produce a vast number of images, which require thorough examination by radiologists. Manual assessment of these images consumes considerable time and may result in inaccuracies in cancer detection. Recently, deep learning has emerged as a reliable tool for decision-making tasks across various domains, including finance, medicine, cybersecurity, agriculture, and forensics. In the context of brain cancer diagnosis, Deep Learning and Machine Learning algorithms applied to MRI data enable rapid prognosis. However, achieving higher accuracy is crucial for providing appropriate treatment to patients and facilitating prompt decision-making by radiologists. To address this, we propose the use of Convolutional Neural Networks (CNN) for brain tumor detection. Our approach utilizes a dataset consisting of two classes: three representing different tumor types and one representing non-tumor samples. We present a model that leverages pre-trained CNNs to categorize brain cancer cases. Additionally, data augmentation techniques are employed to augment the dataset size. The effectiveness of our proposed CNN model is evaluated through various metrics, including validation loss, confusion matrix, and overall loss. The proposed approach employing ResNet50 and EfficientNet demonstrated higher levels of accuracy, precision, and recall in detecting brain tumors.

Latent disentanglement in mesh variational autoencoders improves the diagnosis of craniofacial syndromes and aids surgical planning

Background and objective:The use of deep learning to undertake shape analysis of the complexities of the human head holds great promise. However, there have traditionally been a number of barriers to accurate modelling, especially when operating on both a global and local level. Methods:In this work, we will discuss the application of the Swap Disentangled Variational Autoencoder (SD-VAE) with relevance to Crouzon, Apert and Muenke syndromes. The model is trained on a dataset of 3D meshes of healthy and syndromic patients which was increased in size with a novel data augmentation technique based on spectral interpolation. Thanks to its semantically meaningful and disentangled latent representation, SD-VAE is used to analyse and generate head shapes while considering the influence of different anatomical sub-units. Results:Although syndrome classification is performed on the entire mesh, it is also possible, for the first time, to analyse the influence of each region of the head on the syndromic phenotype. By manipulating specific parameters of the generative model, and producing procedure-specific new shapes, it is also possible to approximate the outcome of a range of craniofacial surgical procedures. Conclusion:This work opens new avenues to advance diagnosis, aids surgical planning and allows for the objective evaluation of surgical outcomes. Our code is available at github.com/simofoti/CraniofacialSD-VAE.

Estimation of human age using machine learning on panoramic radiographs for Brazilian patients

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.

Contrastive prototype network with prototype augmentation for few-shot classification

In recent years, metric-based meta-learning methods have received widespread attention because of their effectiveness in solving few-shot classification problems. However, the scarcity of data frequently results in suboptimal embeddings, causing a discrepancy between anticipated class prototypes and those derived from the support set. These problems severely limit the generalizability of such methods, necessitating further development of Few-Shot Learning (FSL). In this study, we propose the Contrastive Prototype Network (CPN) consisting of three components: (1) Contrastive learning proposed as an auxiliary path to reduce the distance between homogeneous samples and amplify the differences between heterogeneous samples, thereby enhancing the effectiveness and quality of embeddings; (2) A pseudo-prototype strategy proposed to address the bias in prototypes, whereby the pseudo prototypes generated using query set samples are integrated with the initial prototypes to obtain more representative prototypes; (3) A new data augmentation technique, mixupPatch, introduced to alleviate the issue of insufficient data samples, whereby enhanced images are generated by blending the images and labels from different samples, to increase the number of samples. Extensive experiments and ablation studies conducted on five datasets demonstrated that CPN achieves robust results against recent solutions.

Improved Hybrid Bagging Resampling Framework for Deep Learning-Based Side-Channel Analysis

As cryptographic implementations leak secret information through side-channel emissions, the Hamming weight (HW) leakage model is widely used in deep learning profiling side-channel analysis (SCA) attacks to expose the leaked model. However, imbalanced datasets often arise from the HW leakage model, increasing the attack complexity and limiting the performance of deep learning-based SCA attacks. Effective management of class imbalance is vital for training deep neural network models to achieve optimized and improved performance results. Recent works focus on either improved deep-learning methodologies or data augmentation techniques. In this work, we propose the hybrid bagging resampling framework, a two-pronged strategy for tackling class imbalance in side-channel datasets, consisting of data augmentation and ensemble learning. We show that adopting this framework can boost attack performance results in a practical setup. From our experimental results, the SMOTEENN ensemble achieved the best performance in the ASCAD dataset, and the basic ensemble performed the best in the CHES dataset, with both contributing over 70% practical improvements in performance compared to the original imbalanced dataset, and accelerating practical attack space in comparison to the classical setup of the attack.

Cognitive Computing Advancements: Improving Precision Crop Protection through UAV Imagery for Targeted Weed Monitoring

Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was to create an automated multiclass discrimination system using cognitive computing, regardless of the weed growth stage. Initially, the model was trained and tested on a dataset of 31,002 UAV images, including ten weed species manually identified by experts at the early phenological stages of maize (BBCH14) and tomato (BBCH501). The images were captured at 11 m above ground level. This resulted in a classification accuracy exceeding 99.1% using the vision transformer Swin-T model. Subsequently, generative modeling was employed for data augmentation, resulting in new classification models based on the Swin-T architecture. These models were evaluated on an unbalanced dataset of 36,556 UAV images captured at later phenological stages (maize BBCH17 and tomato BBCH509), achieving a weighted average F1-score ranging from 94.8% to 95.3%. This performance highlights the system’s adaptability to morphological variations and its robustness in diverse crop scenarios, suggesting that the system can be effectively implemented in real agricultural scenarios, significantly reducing the time and resources required for weed identification. The proposed data augmentation technique also proved to be effective in implementing the detection transformer architecture, significantly improving the generalization capability and enabling accurate detection of weeds at different growth stages. The research represents a significant advancement in weed monitoring across phenological stages, with potential applications in precision agriculture and sustainable crop management. Furthermore, the methodology showcases the versatility of the latest generation models for application in other knowledge domains, facilitating time-efficient model development. Future research could investigate the applicability of the model in different geographical regions and with different types of crops, as well as real-time implementation for continuous field monitoring.

Enhancing heart disease prediction with reinforcement learning and data augmentation

The study presents a novel method to improve the prediction accuracy of cardiac disease by combining data augmentation techniques with reinforcement learning. The complex nature of cardiac data frequently presents challenges for traditional machine learning models, which results in subpar performance. In response, our fusion methodology improves predictive capabilities by augmenting data and utilizing reinforcement learning's skill at sequential decision-making. Our method predicts cardiac disease with an astounding 94 % accuracy rate, which is an outstanding result. This significant improvement outperforms existing techniques and shows a deeper comprehension of intricate data relationships. The amalgamation of reinforcement learning and data augmentation not only yields superior predictive accuracy but also bears noteworthy consequences for patient care and accurate cardiac diagnosis. Through the efficient combination of these approaches, our method provides a powerful response to the difficulties presented by complicated cardiac data. The potential to transform illness prediction and prevention techniques and ultimately improve patient outcomes is demonstrated by this integration's success.

A lightweight bionic flapping wing drone recognition network based on data enhancement

In recent times, there has been a growing focus on research into bionic drones, which seek to mimic biological behavior and structure, thus overcoming the limitations of conventional drones. The ability of bionic drones to blend into their surroundings presents a significant challenge for identification. This study presents a dataset of bionic drones and introduces the Bionic Drone Identification Network (BDRNet). The dataset was enriched using data augmentation techniques to improve model recognition. Moreover, an Aggregated Attention Mechanism (AAM) captures input feature correlation. Furthermore, a Merged and Integrated Detector Head (MIDHead) and Multi-scale Lightweight Convolution (MLWConv) have been proposed to lessen computational costs. The findings reveal that BDRNet achieves an AP0.5 of 94.4 %, a Params of 2.6 M, and a Flops of 5.5G, surpassing mainstream object detection models such as Faster R-CNN and YoloV5. This suggests that BDRNet demonstrates robust recognition capabilities and holds potential for deployment in resource-constrained embedded devices.

Few-shot Learning using Data Augmentation: A Literature Review

This paper examines few-shot learning, a machine-learning approach that allows models to generalize well with scarce labeled data. It overviews few-shot learning concepts and reviews data augmentation techniques tailored for this scenario. It evaluates these image and object recognition techniques and discusses their benefits and limitations. An experimental setup is described, with selected datasets and metrics to assess the augmentation strategies. Baseline models are analyzed, and the comparative results are presented, identifying key trends and areas for further research in few-shot learning.

Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes

Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge to achieving accurate results. In efforts to overcome this challenge, data augmentation techniques have been proposed. However, the majority of these approaches primarily focus on image generation. For segmentation tasks, providing both images and their corresponding target masks is crucial, and the generation of diverse and realistic samples remains a complex task, especially when working with limited training datasets. To this end, we propose a new end-to-end hybrid architecture based on Hamiltonian Variational Autoencoders (HVAE) and a discriminative regularization to improve the quality of generated images. Our method provides an accurate estimation of the joint distribution of the images and masks, resulting in the generation of realistic medical images with reduced artifacts and off-distribution instances. As generating 3D volumes requires substantial time and memory, our architecture operates on a slice-by-slice basis to segment 3D volumes, capitalizing on the richly augmented dataset. Experiments conducted on two public datasets, BRATS (MRI modality) and HECKTOR (PET modality), demonstrate the efficacy of our proposed method on different medical imaging modalities with limited data.

Advancing breast ultrasound diagnostics through hybrid deep learning models

Today, doctors rely heavily on medical imaging to identify abnormalities. Proper classification of these abnormalities enables them to take informed actions, leading to early diagnosis and treatment. This paper introduces the “EfficientKNN” model, a novel hybrid deep learning approach that combines the advanced feature extraction capabilities of EfficientNetB3 with the simplicity and effectiveness of the k-Nearest Neighbors (k-NN) algorithm. Initially, EfficientNetB3, pre-trained on ImageNet, is repurposed to serve as a feature extractor. Subsequently, a GlobalAveragePooling2D layer is applied, followed by an optional Principal Component Analysis (PCA) to reduce dimensionality while preserving critical information. PCA is used selectively when deemed necessary. The extracted features are then classified using an optimized k-NN algorithm, fine-tuned through meticulous cross-validation.Our model underwent rigorous training using a curated dataset containing benign, malignant, and normal medical images. Data augmentation techniques, including rotations, shifts, flips, and zooms, were employed to help the model generalize and efficiently handle new, unseen data. To enhance the model's ability to identify the important features necessary for accurate predictions, the dataset was refined using segmentation and overlay techniques. The training utilized an ensemble of optimization algorithms—SGD, Adam, and RMSprop—with hyperparameters set at a learning rate of 0.00045, a batch size of 32, and up to 120 epochs, facilitated by early stopping to prevent overfitting.The results demonstrate that the EfficientKNN model outperforms traditional models such as VGG16, AlexNet, and VGG19 in terms of accuracy, precision, and F1-score. Additionally, the model showed better results compared to EfficientNetB3 alone. Achieving a 100 % accuracy rate on multiple tests, the EfficientKNN model has significant potential for real-world diagnostic applications. This study highlights the model's scalability, efficient use of cloud storage, and real-time prediction capabilities, all while minimizing computational demands.By integrating the strengths of EfficientNetB3's deep learning architecture with the interpretability of k-NN, EfficientKNN presents a significant advancement in medical image classification, promising improved diagnostic accuracy and clinical applicability.

Enhancing Pulmonary Diagnosis in Chest X-rays through Generative AI Techniques

Chest X-ray imaging is an essential tool in the diagnostic procedure for pulmonary conditions, providing healthcare professionals with the capability to immediately and accurately determine lung anomalies. This imaging modality is fundamental in assessing and confirming the presence of various lung issues, allowing for timely and effective medical intervention. In response to the widespread prevalence of pulmonary infections globally, there is a growing imperative to adopt automated systems that leverage deep learning (DL) algorithms. These systems are particularly adept at handling large radiological datasets and providing high precision. This study introduces an advanced identification model that utilizes the VGG16 architecture, specifically adapted for identifying various lung anomalies such as opacity, COVID-19 pneumonia, normal appearance of the lungs, and viral pneumonia. Furthermore, we address the issue of model generalizability, which is of prime significance in our work. We employed the data augmentation technique through CycleGAN, which, through experimental outcomes, has proven effective in enhancing the robustness of our model. The combined performance of our advanced VGG model with the CycleGAN augmentation technique demonstrates remarkable outcomes in several evaluation metrics, including recall, F1-score, accuracy, precision, and area under the curve (AUC). The results of the advanced VGG16 model showcased remarkable accuracy, achieving 98.58%. This study contributes to advancing generative artificial intelligence (AI) in medical imaging analysis and establishes a solid foundation for ongoing developments in computer vision technologies within the healthcare sector.

Advancing precision agriculture: domain-specific augmentations and robustness testing for convolutional neural networks in precision spraying evaluation

AbstractModern agriculture relies heavily on the precise application of chemicals such as fertilisers, herbicides, and pesticides, which directly affect both crop yield and environmental footprint. Therefore, it is crucial to assess the accuracy of precision sprayers regarding the spatial location of spray deposits. However, there is currently no fully automated evaluation method for this. In this study, we collected a novel dataset from a precision spot spraying system to enable us to classify and detect spray deposits on target weeds and non-target crops. We employed multiple deep convolutional backbones for this task; subsequently, we have proposed a robustness testing methodology for evaluation purposes. We experimented with two novel data augmentation techniques: subtraction and thresholding which enhanced the classification accuracy and robustness of the developed models. On average, across nine different tests and four distinct convolutional neural networks, subtraction improves robustness by 50.83%, and thresholding increases by 42.26% from a baseline. Additionally, we have presented the results from a novel weakly supervised object detection task using our dataset, establishing a baseline Intersection over Union score of 42.78%. Our proposed pipeline includes an explainable artificial intelligence stage and provides insights not only into the spatial location of the spray deposits but also into the specific filtering methods within that spatial location utilised for classification.

A Survey on Deep Learning Techniques for Medical Image Fusion

Medical Image Fusion is a process that involves combining information from multiple medical images, which is essential for healthcare applications like diagnosis, treatment planning, and image-guided interventions. Deep learning techniques have shown significant promise in medical image fusion by integrating information and effectively capturing data from diverse modalities. Additionally, Data augmentation techniques have come to be an important tool for improving model performance and generalization. The objective of this paper is to give a comprehensive overview of deep learning techniques and data augmentation methods utilized in medical image fusion from 2018 to 2023. The paper covers a variety of topics such as image registration, feature extraction, fusion architectures, data augmentation techniques, and evaluation metrics. The survey also discusses the challenges, limitations, and future directions in the field.

Lightweight safflower cluster detection based on YOLOv5

The effective detection of safflower in the field is crucial for implementing automated visual navigation and harvesting systems. Due to the small physical size of safflower clusters, their dense spatial distribution, and the complexity of field scenes, current target detection technologies face several challenges in safflower detection, such as insufficient accuracy and high computational demands. Therefore, this paper introduces an improved safflower target detection model based on YOLOv5, termed Safflower-YOLO (SF-YOLO). This model employs Ghost_conv to replace traditional convolution blocks in the backbone network, significantly enhancing computational efficiency. Furthermore, the CBAM attention mechanism is integrated into the backbone network, and a combined LCIOU+NWD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$L_{CIOU + NWD}$$\\end{document} loss function is introduced to allow for more precise feature extraction, enhanced adaptive fusion capabilities, and accelerated loss convergence. Anchor boxes, updated through K-means clustering, are used to replace the original anchors, enabling the model to better adapt to the multi-scale information of safflowers in the field. Data augmentation techniques such as Gaussian blur, noise addition, sharpening, and channel shuffling are applied to the dataset to maintain robustness against variations in lighting, noise, and visual angles. Experimental results demonstrate that SF-YOLO surpasses the original YOLOv5s model, with reductions in GFlops and Params from 15.8 to 13.2 G and 7.013 to 5.34 M, respectively, representing decreases of 16.6% and 23.9%. Concurrently, SF-YOLO’s mAP0.5 increases by 1.3%, reaching 95.3%. This work enhances the accuracy of safflower detection in complex agricultural environments, providing a reference for subsequent autonomous visual navigation and automated non-destructive harvesting technologies in safflower operations.

Federated regressive learning: Adaptive weight updates through statistical information of clients

Federated learning is a method for training models in a distributed environment where each client utilizes its local dataset to train a model and shares it with a server to create a global model. This approach offers various advantages, such as eliminating the need for local clients to directly transfer their data to the server. However, it also has limitations, including the potential for limited global model performance due to imbalanced local datasets. In this study, we propose a new model that evaluates the quality of each client’s local dataset based on its probability distribution and quantity. Based on this evaluation, the contribution of locally trained models to the global model is adjusted accordingly. Unlike previous studies, our proposed method addresses the issue of low-quality local models degrading the performance of the global model. This allows for the improvement of the global model’s performance without the need for complex processes, such as adding specially designed loss functions to local models or employing data augmentation techniques to enhance the quality of the dataset. Furthermore, the method of calculating the contribution of local models proposed in this study allows for the quantitative assessment of the performance of clients participating in federated learning based on their data collection capabilities, making it an industry-friendly approach. We conducted experiments using various benchmark datasets, simulating different scenarios that could occur in the real world. In most scenarios, except a few, our method showed superior performance on all evaluation metrics compared to existing studies reviewed in the paper.

LI3D-BiLSTM: A Lightweight Inception-3D Networks with BiLSTM for Video Action Recognition

This paper proposes an improved video action recognition method, primarily consisting of three key components. Firstly, in the data preprocessing stage, we developed multi-temporal scale video frame extraction and multi-spatial scale video cropping techniques to enhance content information and standardize input formats. Secondly, we propose a lightweight Inception-3D networks (LI3D) network structure for spatio-temporal feature extraction and design a soft-association feature aggregation module to improve the recognition accuracy of key actions in videos. Lastly, we employ a bidirectional LSTM network to contextualize the feature sequences extracted by LI3D, enhancing the representation capability for temporal data. To improve the model’s robustness and generalization ability, we introduced spatial and temporal scale data augmentation techniques in the preprocessing stage, effectively extracting video key frames and capturing key regional actions. Furthermore, we conducted an in-depth study on spatio-temporal feature extraction methods for video data, effectively extracting spatial and temporal information through the LI3D network and transfer learning. Experimental results demonstrate that the proposed method achieves significant performance improvements in video action recognition tasks, providing new insights and approaches for research in related fields.

Improved Fault Detection Using Shifting Window Data Augmentation of Induction Motor Current Signals

Deep learning models have demonstrated potential in Condition-Based Monitoring (CBM) for rotating machinery, such as induction motors (IMs). However, their performance is significantly influenced by the size of the training dataset and the way signals are presented to the model. When trained on segmented signals over a fixed period, the model’s accuracy can decline when tested on signals that differ from the training interval or are randomly sampled. Conversely, models utilizing data augmentation techniques exhibit better generalization to unseen conditions. This paper highlights the bias introduced by traditional training methods towards specific periodic waveform sampling and proposes a new method to augment phase current signals during training using a shifting window technique. This approach is considered as a practical approach for motor current augmentation and is shown to enhance classification accuracy and improved generalization when compared to existing techniques.

Foam detection in a stirred tank using deep learning neural networks

A deep learning method based on the Convolutional Neural Network (CNN) U-Net architecture has been explored as an image analysis tool for the detection and automated quantification of foam generated in a stirred tank. Multiple datasets of different sizes and with different types of foam were obtained experimentally and processed with data augmentation techniques in order to train the deep learning networks. The impact of adding attention and residual gates to the U-Net model to improve its performance, as well as the size of the dataset used to train the model have been assessed. The main factor influencing the accuracy of the models to detect the foam in the stirred tank is the size and quality of the dataset use to train the U-Net models; it must be sufficiently large and varied in terms of foam type/structure. The addition of attention gates and residual blocks to the U-Net model slightly improves the accuracy of foam detection, particularly for images that contain additional objects or obstructions, however the training time is 30 % longer than the standard U-Net model. In all cases, this image analysis method based on the U-Net model largely out performs conventional image analysis, which was not able to automatically differentiate between foam, additional objects in the tank and bubbles in the liquid. Two methods for the measurement of foam quantity (maximum foam height and foam volume) have also been developed based on the foam regions detected by the models. This is important for the application of the tool, which is to be used to understand the impact of operating conditions on foam formation in lab-/pilot-scale stirred tanks and then develop scale-up guidelines for foam control in industrial tanks.