Reliable deep learning framework for the ground penetrating radar data to locate the horizontal variation in levee soil compaction

Alzubaidi, Laith; Chlaib, Hussein Khalefa; Fadhel, Mohammed A.; Chen, Yubo; Bai, Jinshuai; Albahri, A.S.; Gu, Yuantong

doi:10.1016/j.engappai.2023.107627

Cited by 14 publications

(2 citation statements)

References 28 publications

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“…Otherwise, the performance of ML classifiers is poor. Feature fusion is crucial in DL as it allows neural networks to combine and integrate information from multiple sources or layers, permitting them to capture complex patterns and relationships within the data [ 33 , 34 ]. It enhances the model’s ability to make more accurate and robust predictions across various tasks, ultimately improving the performance and generalisation of DL models [ 35 ].…”

Section: Introductionmentioning

confidence: 99%

Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images

Alzubaidi,

Salhi,

A.Fadhel

et al. 2024

PLoS ONE

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Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen’s kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.

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Section: Introductionmentioning

confidence: 99%

Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images

Alzubaidi,

Salhi,

A.Fadhel

et al. 2024

PLoS ONE

Self Cite

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“…In this study, we introduce LeishFuNet, a deep learning model designed for the detection of Leishmania patients from their microscopic images. Leveraging transfer learning, specifically employing a feature fusion technique known to be beneficial for models trained on small-sized datasets [ 31 – 33 ], our model demonstrates promising capabilities in this domain. The key contributions of our research are as follows:…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine

Sadeghi,

Fakhar

et al. 2024

BMC Infect Dis

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Background Leishmaniasis, an illness caused by protozoa, accounts for a substantial number of human fatalities globally, thereby emerging as one of the most fatal parasitic diseases. The conventional methods employed for detecting the Leishmania parasite through microscopy are not only time-consuming but also susceptible to errors. Therefore, the main objective of this study is to develop a model based on deep learning, a subfield of artificial intelligence, that could facilitate automated diagnosis of leishmaniasis. Methods In this research, we introduce LeishFuNet, a deep learning framework designed for detecting Leishmania parasites in microscopic images. To enhance the performance of our model through same-domain transfer learning, we initially train four distinct models: VGG19, ResNet50, MobileNetV2, and DenseNet 169 on a dataset related to another infectious disease, COVID-19. These trained models are then utilized as new pre-trained models and fine-tuned on a set of 292 self-collected high-resolution microscopic images, consisting of 138 positive cases and 154 negative cases. The final prediction is generated through the fusion of information analyzed by these pre-trained models. Grad-CAM, an explainable artificial intelligence technique, is implemented to demonstrate the model’s interpretability. Results The final results of utilizing our model for detecting amastigotes in microscopic images are as follows: accuracy of 98.95 1.4%, specificity of 98 2.67%, sensitivity of 100%, precision of 97.91 2.77%, F1-score of 98.92 1.43%, and Area Under Receiver Operating Characteristic Curve of 99 1.33. Conclusion The newly devised system is precise, swift, user-friendly, and economical, thus indicating the potential of deep learning as a substitute for the prevailing leishmanial diagnostic techniques.

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SSP: self-supervised pertaining technique for classification of shoulder implants in x-ray medical images: a broad experimental study

Alzubaidi,

Fadhel,

Hollman

et al. 2024

Artif Intell Rev

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Multiple pathologic conditions can lead to a diseased and symptomatic glenohumeral joint for which total shoulder arthroplasty (TSA) replacement may be indicated. The long-term survival of implants is limited. With the increasing incidence of joint replacement surgery, it can be anticipated that joint replacement revision surgery will become more common. It can be challenging at times to retrieve the manufacturer of the in situ implant. Therefore, certain systems facilitated by AI techniques such as deep learning (DL) can help correctly identify the implanted prosthesis. Correct identification of implants in revision surgery can help reduce perioperative complications and complications. DL was used in this study to categorise different implants based on X-ray images into four classes (as a first case study of the small dataset): Cofield, Depuy, Tornier, and Zimmer. Imbalanced and small public datasets for shoulder implants can lead to poor performance of DL model training. Most of the methods in the literature have adopted the idea of transfer learning (TL) from ImageNet models. This type of TL has been proven ineffective due to some concerns regarding the contrast between features learnt from natural images (ImageNet: colour images) and shoulder implants in X-ray images (greyscale images). To address that, a new TL approach (self-supervised pertaining (SSP)) is proposed to resolve the issue of small datasets. The SSP approach is based on training the DL models (ImageNet models) on a large number of unlabelled greyscale medical images in the domain to update the features. The models are then trained on a small labelled data set of X-ray images of shoulder implants. The SSP shows excellent results in five ImageNet models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, and EfficientNet with precision of 96.69%, 95.45%, 98.76%, 98.35%, and 96.6%, respectively. Furthermore, it has been shown that different domains of TL (such as ImageNet) do not significantly affect the performance of shoulder implants in X-ray images. A lightweight model trained from scratch achieves 96.6% accuracy, which is similar to using standard ImageNet models. The features extracted by the DL models are used to train several ML classifiers that show outstanding performance by obtaining an accuracy of 99.20% with Xception+SVM. Finally, extended experimentation has been carried out to elucidate our approach’s real effectiveness in dealing with different medical imaging scenarios. Specifically, five different datasets are trained and tested with and without the proposed SSP, including the shoulder X-ray with an accuracy of 99.47% and CT brain stroke with an accuracy of 98.60%.

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Reliable deep learning framework for the ground penetrating radar data to locate the horizontal variation in levee soil compaction

Cited by 14 publications

References 28 publications

Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images

Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images

A deep learning-based model for detecting Leishmania amastigotes in microscopic slides: a new approach to telemedicine

SSP: self-supervised pertaining technique for classification of shoulder implants in x-ray medical images: a broad experimental study

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