CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19

Ornob, Tareque Rahman; Roy, Gourab; Hassan, Enamul

doi:10.1016/j.imu.2022.101156

Cited by 8 publications

(6 citation statements)

References 36 publications

(43 reference statements)

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“…We consider this to be very important to obtain good prediction which can support the fusion process which depends on the predicted label and the multimodality features. On the other hand, the work reported in 39 is rather an ensemble of neural networks used to build triplet Siamese network. While ensemble method has reported good performance in literature, we note that this can result in a very staggering dimension of features which needs to be considered during multimodality fusion.…”

Section: Resultsmentioning

confidence: 99%

“…Word vector processing was achieved by the study using Word2Vec method, and an attention mechanism for assigning weights values to keywords in questions. Similarly, a triplet Siamese neural networks which uses few-shot learning algorithms have been investigated in 39 . The study leverages on the benefit of few-shot learning which is capable of effectively learning features from small dataset, to address the problem of detecting COVID-19 CT scan images.…”

Section: Related Workmentioning

confidence: 99%

“…As a result, we studied other related Siamese networks to observe current gaps in literature with regards to solving feature fusion learning. For instance, triplet Siamese network have been applied to unimodal CT scan images 39 , one-shot Siamese network have solved unimodal classification problem using hyperspectral images 40 , Siamese neural network and k-nearest neighbor (K-NN) model have been used on unimodal brain MRI for neighborhood analysis 41 . Also, the work of 42 have applied twin neural network in comparing a query versus database samples of optical coherent tomography (OCT) scans for similarity check.…”

Section: Introductionmentioning

confidence: 99%

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A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

Oyelade,

Irunokhai,

Wang

2024

Sci Rep

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There is a wide application of deep learning technique to unimodal medical image analysis with significant classification accuracy performance observed. However, real-world diagnosis of some chronic diseases such as breast cancer often require multimodal data streams with different modalities of visual and textual content. Mammography, magnetic resonance imaging (MRI) and image-guided breast biopsy represent a few of multimodal visual streams considered by physicians in isolating cases of breast cancer. Unfortunately, most studies applying deep learning techniques to solving classification problems in digital breast images have often narrowed their study to unimodal samples. This is understood considering the challenging nature of multimodal image abnormality classification where the fusion of high dimension heterogeneous features learned needs to be projected into a common representation space. This paper presents a novel deep learning approach combining a dual/twin convolutional neural network (TwinCNN) framework to address the challenge of breast cancer image classification from multi-modalities. First, modality-based feature learning was achieved by extracting both low and high levels features using the networks embedded with TwinCNN. Secondly, to address the notorious problem of high dimensionality associated with the extracted features, binary optimization method is adapted to effectively eliminate non-discriminant features in the search space. Furthermore, a novel method for feature fusion is applied to computationally leverage the ground-truth and predicted labels for each sample to enable multimodality classification. To evaluate the proposed method, digital mammography images and digital histopathology breast biopsy samples from benchmark datasets namely MIAS and BreakHis respectively. Experimental results obtained showed that the classification accuracy and area under the curve (AUC) for the single modalities yielded 0.755 and 0.861871 for histology, and 0.791 and 0.638 for mammography. Furthermore, the study investigated classification accuracy resulting from the fused feature method, and the result obtained showed that 0.977, 0.913, and 0.667 for histology, mammography, and multimodality respectively. The findings from the study confirmed that multimodal image classification based on combination of image features and predicted label improves performance. In addition, the contribution of the study shows that feature dimensionality reduction based on binary optimizer supports the elimination of non-discriminant features capable of bottle-necking the classifier.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

Oyelade,

Irunokhai,

Wang

2024

Sci Rep

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show abstract

“…In the study conducted by Ornob et al [32], they developed a detection method for COVID-19 by creating an ensemble of six pre-trained CNNs and the Vision Image Transformer (ViT), specifically the Swin Transformer, along with a Triplet Siamese Neural Network framework. Although we have concerns about the computational resources required, the concept of integrating cutting-edge models to achieve optimal results in the medical field is acceptable.…”

Section: Discussionmentioning

confidence: 99%

“…Ornob et al [32] introduced a Siamese few-shot learning model for early detection of COVID-19, aiming to mitigate the long-term effects of this dangerous disease. Their proposed architecture combined few-shot learning with an ensemble of pre-trained Convolutional Neural Networks, enabling the extraction of feature vectors from CT scan images for similarity learning.…”

Section: Related Workmentioning

confidence: 99%

DFU-Helper: An Innovative Framework for Longitudinal Diabetic Foot Ulcer Diseases Evaluation Using Deep Learning

Toofanee,

Dowlut,

Hamroun

et al. 2023

Applied Sciences

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Diabetes affects roughly 537 million people, and is predicted to reach 783 million by 2045. Diabetes Foot Ulcer (DFU) is a major complication associated with diabetes and can lead to lower limb amputation. The rapid evolution of diabetic foot ulcers (DFUs) necessitates immediate intervention to prevent the severe consequences of amputation and related complications. Continuous and meticulous patient monitoring for individuals with diabetic foot ulcers (DFU) is crucial and is currently carried out by medical practitioners on a daily basis. This research article introduces DFU-Helper, a novel framework that employs a Siamese Neural Network (SNN) for accurate and objective assessment of the progression of diabetic foot ulcers (DFUs) over time. DFU-Helper provides healthcare professionals with a comprehensive visual and numerical representation in terms of the similarity distance of the disease, considering five distinct disease conditions: none, infection, ischemia, both (presence of ischemia and infection), and healthy. The SNN achieves the best Macro F1-score of 0.6455 on the test dataset when applying pseudo-labeling with a pseudo-threshold set to 0.9. The SNN is used in the process of creating anchors for each class using feature vectors. When a patient initially consults a healthcare professional, an image is transmitted to the model, which computes the distances from each class anchor point. It generates a comprehensive table with corresponding figures and a visually intuitive radar chart. In subsequent visits, another image is captured and fed into the model alongside the initial image. DFU-Helper then plots both images and presents the distances from the class anchor points. Our proposed system represents a significant advancement in the application of deep learning for the longitudinal assessment of DFU. To the best of our knowledge, no existing tool harnesses deep learning for DFU follow-up in a comparable manner.

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Few-shot learning for COVID-19 chest X-ray classification with imbalanced data: an inter vs. intra domain study

Galán-Cuenca,

Gallego,

Saval-Calvo

et al. 2024

Pattern Anal Applic

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Medical image datasets are essential for training models used in computer-aided diagnosis, treatment planning, and medical research. However, some challenges are associated with these datasets, including variability in data distribution, data scarcity, and transfer learning issues when using models pre-trained from generic images. This work studies the effect of these challenges at the intra- and inter-domain level in few-shot learning scenarios with severe data imbalance. For this, we propose a methodology based on Siamese neural networks in which a series of techniques are integrated to mitigate the effects of data scarcity and distribution imbalance. Specifically, different initialization and data augmentation methods are analyzed, and four adaptations to Siamese networks of solutions to deal with imbalanced data are introduced, including data balancing and weighted loss, both separately and combined, and with a different balance of pairing ratios. Moreover, we also assess the inference process considering four classifiers, namely Histogram, kNN, SVM, and Random Forest. Evaluation is performed on three chest X-ray datasets with annotated cases of both positive and negative COVID-19 diagnoses. The accuracy of each technique proposed for the Siamese architecture is analyzed separately. The results are compared to those obtained using equivalent methods on a state-of-the-art CNN, achieving an average F1 improvement of up to 3.6%, and up to 5.6% of F1 for intra-domain cases. We conclude that the introduced techniques offer promising improvements over the baseline in almost all cases and that the technique selection may vary depending on the amount of data available and the level of imbalance.

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CovidExpert: A Triplet Siamese Neural Network framework for the detection of COVID-19

Cited by 8 publications

References 36 publications

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

A twin convolutional neural network with hybrid binary optimizer for multimodal breast cancer digital image classification

DFU-Helper: An Innovative Framework for Longitudinal Diabetic Foot Ulcer Diseases Evaluation Using Deep Learning

Few-shot learning for COVID-19 chest X-ray classification with imbalanced data: an inter vs. intra domain study

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