Fuzzy Metadata Augmentation for Multimodal Data Classification

Gordienko, Yuri; Shulha, Maksym; Kochura, Yuriy; Rokovyi, Oleksandr; Alienin, Oleg; Stirenko, Sergii

doi:10.1007/978-981-99-0835-6_11

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…Each dataset has been partitioned into segments of 7:1:2 (train:val:test)/9:1 (train: val) 23 . In the studies and performance evaluations using this dataset in the literature, no resizing was done and the images were used with the same size 24–29 …”

Section: Methodsmentioning

confidence: 99%

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MResCaps: Enhancing capsule networks with parallel lanes and residual blocks for high‐performance medical image classification

Şengül,

Özkan

2024

Int J Imaging Syst Tech

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The classification of medical images enables physicians to perform expeditious and accurate data analysis, increasing the chances of timely disease diagnosis and early intervention to the patient. However, classification is a time‐consuming and labour intensive process when done manually. The Capsule Network (CapsNet) architecture has advantages in accurately and quickly classifying medical images due to its ability to evaluate images within part‐whole relationships, robustness to data rotations and affine transformations, and good performance on small datasets. However, CapsNet may demonstrate low performance on complex datasets. In this study, a new CapsNet model named MResCaps is proposed to overcome this disadvantage and enhance its performance on complex images. MResCaps utilizes an increasing number of residual blocks in each layer in parallel lane to obtain rich feature maps at different levels, aiming to achieve high success in the classification of various medical images. To evaluate the model's performance, the CIFAR10 dataset and the DermaMNIST, PneumoniaMNIST, and OrganMNIST‐S datasets from the MedMNIST dataset collection are used. MResCaps outperformed CapsNet by 20% in terms of accuracy on the CIFAR10 dataset. In addition, AUC values of 96.25%, 96.30%, and 97.12% were achieved in DermaMNIST, PneumoniaMNIST, and OrganMNIST‐S datasets, respectively. The results show that the proposed new model MResCaps improves the performance of CapsNet in the classification of complex and medical images. Furthermore, the model has demonstrated a better performance in comparison with extant studies in the literature. This study aims to contribute significantly to the literature by introducing a novel perspective on CapsNet‐based architectures for the classification of medical images through a parallel‐laned architecture and a rich feature capsule‐focused approach.

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Section: Methodsmentioning

confidence: 99%

“…23 In the studies and performance evaluations using this dataset in the literature, no resizing was done and the images were used with the same size. [24][25][26][27][28][29]…”

Section: Datasetsmentioning

confidence: 99%

MResCaps: Enhancing capsule networks with parallel lanes and residual blocks for high‐performance medical image classification

Şengül,

Özkan

2024

Int J Imaging Syst Tech

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“…Additionally, the authors mapped the main image quality variables to terms used in the General Image-Quality Equation (GIQE)namely, ground sample distance (GSD), relative edge response (RER), and signal-to-noise ratio (SNR)-and assessed the applicability of the GIQE function form to modeling target detector performance in the presence of significant image distortion. Gordienko et al [22] studied the performance of target detection on multimodal satellite images using the Vehicle Detection in Aerial Imagery (VEDAI) dataset, adopting the YOLO (You Only Look Once) framework, covering RGB, IR, and RGB + IR modalities, as well as different image sizes ranging from 128 × 128 to 1024 × 1024. The evaluation method included 10-fold cross-validation to ensure the model's generalization ability, mainly relying on the average precision (mAP) metric, especially mAP@0.5 at an IoU (intersection over union) threshold of 0.5, as well as the mAP range from 0.5 to 0.95; this shows that through this hybrid approach, mAP can be significantly improved at specific image sizes, providing valuable data-driven insights into how to optimize target detection systems.…”

Section: Introductionmentioning

confidence: 99%

Multidimensional Evaluation Methods for Deep Learning Models in Target Detection for SAR Images

Wang,

Liu,

Zhou

et al. 2024

Remote Sensing

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As artificial intelligence technology advances, the application of object detection technology in the field of SAR (synthetic aperture radar) imagery is becoming increasingly widespread. However, it also faces challenges such as resource limitations in spaceborne environments and significant uncertainty in the intensity of interference in application scenarios. These factors make the performance evaluation of object detection key to ensuring the smooth execution of tasks. In the face of such complex and harsh application scenarios, methods that rely on single-dimensional evaluation to assess models have had their limitations highlighted. Therefore, this paper proposes a multi-dimensional evaluation method for deep learning models used in SAR image object detection. This method evaluates models in a multi-dimensional manner, covering the training, testing, and application stages of the model, and constructs a multi-dimensional evaluation index system. The training stage includes assessing training efficiency and the impact of training samples; the testing stage includes model performance evaluation, application-based evaluation, and task-based evaluation; and the application stage includes model operation evaluation and model deployment evaluation. The evaluations of these three stages constitute the key links in the performance evaluation of deep learning models. Furthermore, this paper proposes a multi-indicator comprehensive evaluation method based on entropy weight correlation scaling, which calculates the weights of each evaluation indicator through test data, thereby providing a balanced and comprehensive evaluation mechanism for model performance. In the experiments, we designed specific interferences for SAR images in the testing stage and tested three models from the YOLO series. Finally, we constructed a multi-dimensional performance profile diagram for deep learning object detection models, providing a new visualization method to comprehensively characterize model performance in complex application scenarios. This can provide more accurate and comprehensive model performance evaluation for remote sensing data processing, thereby guiding model selection and optimization. The evaluation method proposed in this study adopts a multi-dimensional perspective, comprehensively assessing the three core stages of a model’s lifecycle: training, testing, and application. This framework demonstrates significant versatility and adaptability, enabling it to transcend the boundaries of remote sensing technology and provide support for a wide range of model evaluation and optimization tasks.

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