AdaD-FNN for Chest CT-Based COVID-19 Diagnosis

Yao, Xujing; Zhu, Ziquan; Cheng, Kang; Wang, Shuihua; Górriz, Juan Manuel; Zhang, Yudong

doi:10.1109/tetci.2022.3174868

Cited by 23 publications

(7 citation statements)

References 55 publications

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“…The method we propose employs a model fusion-based approach, where we merge the classification results from multiple models and subsequently select the most accurate one. Prior studies have proposed ensemble learning based methods, such as fusion techniques and methods related to boosting techniques 28 , 29 . The "Cost-sensitive Boosting Pruning Trees" methodology involves feature extraction from diverse data sources 28 .…”

Section: Discussionmentioning

confidence: 99%

“…However, this method is not tailored for image classification. Conversely, "AdaD-FNN" employs a fuzzy stacking approach to enhance features extracted from Chest Computed Tomography (CCT) images, improving classification accuracy 29 . Both these approaches are geared towards augmenting classification accuracy through data feature extraction or enhancement.…”

Section: Discussionmentioning

confidence: 99%

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Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation

Juan,

Su,

et al. 2023

Sci Rep

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We aimed to develop an accurate and efficient skin cancer classification system using deep-learning technology with a relatively small dataset of clinical images. We proposed a novel skin cancer classification method, SkinFLNet, which utilizes model fusion and lifelong learning technologies. The SkinFLNet's deep convolutional neural networks were trained using a dataset of 1215 clinical images of skin tumors diagnosed at Taichung and Taipei Veterans General Hospital between 2015 and 2020. The dataset comprised five categories: benign nevus, seborrheic keratosis, basal cell carcinoma, squamous cell carcinoma, and malignant melanoma. The SkinFLNet's performance was evaluated using 463 clinical images between January and December 2021. SkinFLNet achieved an overall classification accuracy of 85%, precision of 85%, recall of 82%, F-score of 82%, sensitivity of 82%, and specificity of 93%, outperforming other deep convolutional neural network models. We also compared SkinFLNet's performance with that of three board-certified dermatologists, and the average overall performance of SkinFLNet was comparable to, or even better than, the dermatologists. Our study presents an efficient skin cancer classification system utilizing model fusion and lifelong learning technologies that can be trained on a relatively small dataset. This system can potentially improve skin cancer screening accuracy in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation

Juan,

Su,

et al. 2023

Sci Rep

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“…Recent studies use the above general CNN-based backbone networks with some modifications [7], [8]. An ensemble model is also used for optimization [21].…”

Section: A Cnn and Transformermentioning

confidence: 99%

Self-Supervised Monocular Depth Estimation Using Hybrid Transformer Encoder

et al. 2022

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Depth estimation using monocular camera sensors is an important technique in computer vision. Supervised monocular depth estimation requires a lot of data acquired from depth sensors. However, acquiring depth data is an expensive task. We sometimes cannot acquire data due to the limitations of the sensor. View synthesisbased depth estimation research is a self-supervised learning method that does not require depth data supervision. Previous studies mainly use CNN-based networks in encoders. CNN is suitable for extracting local features through convolution operation. Recent vision transformers are suitable for global feature extraction based on multi-self-attention modules. In this paper, we propose a hybrid network combining CNN and vision transformer network in self-supervised learning-based monocular depth estimation. We design an encoder-decoder structure that uses CNNs in the earlier stage of extracting local features and a vision transformer in the later stages of extracting global features. We evaluate the proposed network through various experiments based on KITTI and Cityscapes datasets. The results showed higher performance than previous studies and reduced parameters and computations. Codes and trained models are available at https://github.com/fogfog2/manydepthformer.

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“…Recently, deep learning has been widely applied in solving real-world problems, e.g., COVID-19 diagnosis [9]. Our work is mainly relevant to deep learning based visual positioning, deep learning based visual-inertial odometry and attention mechanisms.…”

Section: Related Workmentioning

confidence: 99%

EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention

Tu¹,

Chen²,

Pan³

et al. 2022

Preprint

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Accurate and robust localization is a fundamental need for mobile agents. Visual-inertial odometry (VIO) algorithms exploit the information from camera and inertial sensors to estimate position and translation. Recent deep learning based VIO models attract attentions as they provide pose information in a data-driven way, without the need of designing hand-crafted algorithms. Existing learning based VIO models rely on recurrent models to fuse multimodal data and process sensor signal, which are hard to train and not efficient enough. We propose a novel learning based VIO framework with external memory attention that effectively and efficiently combines visual and inertial features for states estimation. Our proposed model is able to estimate pose accurately and robustly, even in challenging scenarios, e.g., on overcast days and water-filled ground , which are difficult for traditional VIO algorithms to extract visual features. Experiments validate that it outperforms both traditional and learning based VIO baselines in different scenes.

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AdaD-FNN for Chest CT-Based COVID-19 Diagnosis

Cited by 23 publications

References 55 publications

Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation

Deep convolutional neural network with fusion strategy for skin cancer recognition: model development and validation

Self-Supervised Monocular Depth Estimation Using Hybrid Transformer Encoder

EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention

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