A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

Al-Timemy, Ali H.; Alzubaidi, Laith; Mosa, Zahraa M.; Abdelmotaal, Hazem; Ghaeb, Nebras H.; Lavric, Alexandru; Hazarbassanov, Rossen Mihaylov; Takahashi, Hidenori; Gu, YuanTong; Yousefi, Siamak

doi:10.3390/diagnostics13101689

Cited by 16 publications

(7 citation statements)

References 27 publications

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“…Some research work has been done based on transfer learning for classifying KCN. These studies [14][15][16][17][18][19][20][21][22][23][24] have used different pretrained convolutional neural network (CNN) models such as VGG16, VGG19, InceptionV3, ResNet152, InceptionResNetV2, SqueezeNet, AlexNet, ShuffleNet and MobileNetv2. In Al-Timemy et al, 14 the authors introduced a method called dnsemble of deep transfer learning (EDTL) to detect KCN.…”

Section: Related Workmentioning

confidence: 99%

“…The study achieved an accuracy of 99.33% and used a dataset consisting of 3000 images based on 1 map, with 2 classes being classified and in Kamiya et al , 18 a transfer learning approach was applied to the ResNet18 model and achieved 99.1% accuracy. In Al-Timemy et al , 19 a deep learning model using Xception and InceptionResNetV2 architectures was proposed for the early detection of clinical KCN. By fusing the extracted features, the model was able to effectively detect subclinical forms of KCN with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach

Yaraghi,

Khatibi

2024

BMJ Open Ophth

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ObjectiveOur objective is to develop a novel keratoconus image classification system that leverages multiple pretrained models and a transformer architecture to achieve state-of-the-art performance in detecting keratoconus.Methods and analysisThree pretrained models were used to extract features from the input images. These models have been trained on large datasets and have demonstrated strong performance in various computer vision tasks.The extracted features from the three pretrained models were fused using a feature fusion technique. This fusion aimed to combine the strengths of each model and capture a more comprehensive representation of the input images. The fused features were then used as input to a vision transformer, a powerful architecture that has shown excellent performance in image classification tasks. The vision transformer learnt to classify the input images as either indicative of keratoconus or not.The proposed method was applied to the Shahroud Cohort Eye collection and keratoconus detection dataset. The performance of the model was evaluated using standard evaluation metrics such as accuracy, precision, recall and F1 score.ResultsThe research results demonstrated that the proposed model achieved higher accuracy compared with using each model individually.ConclusionThe findings of this study suggest that the proposed approach can significantly improve the accuracy of image classification models for keratoconus detection. This approach can serve as an effective decision support system alongside physicians, aiding in the diagnosis of keratoconus and potentially reducing the need for invasive procedures such as corneal transplantation in severe cases.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach

Yaraghi,

Khatibi

2024

BMJ Open Ophth

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“…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 ]. Moreover, it is essential to address the problem of data scarcity before utilising feature fusion techniques [ 36 ]. Lastly, most studies on the detection of shoulder abnormalities have not evaluated the performance of the models used to explain the “black box” of DL.…”

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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“…Ophthalmology, one of the most imaging intensive fields of medicine, has witnessed a significant transformation with the emergence of AI-powered diagnostic tools 6 – 8 . However, AI applications in the anterior segment parts of the eye including cornea 9 – 12 have received less attention compared to the AI applications in posterior segment of the eye including retina. 13 – 16 …”

Section: Introductionmentioning

confidence: 99%

“…Ophthalmology, one of the most imaging intensive fields of medicine, has witnessed a significant transformation with the emergence of AI-powered diagnostic tools [6][7][8] . However, AI applications in the anterior segment parts of the eye including cornea [9][10][11][12] have received less attention compared to the AI applications in posterior segment of the eye including retina. [13][14][15][16] Among AI tools, ChatGPT, a cutting-edge large language model (LLM) developed by OpenAI (San Francisco, California), has recently received attention, and holds great potential for comprehending clinical expertise and delivering relevant information 17,18 .…”

Section: Introductionmentioning

confidence: 99%

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases

Delsoz,

Madadi,

Munir

et al. 2023

Preprint

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IntroductionAssessing the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.MethodsWe randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, degenerations, and injuries from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses then compared with the diagnoses of three cornea specialists (Human experts) and evaluated interobserver agreements.ResultsThe provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct out of 20 cases) while the accuracy of ChatGPT-3.5 was 60% (12 correct cases out of 20). The accuracy of three cornea specialists were 100% (20 cases), 90% (18 cases), and 90% (18 cases), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases) while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of three cornea specialists was 60% (12 cases).ConclusionsThe accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration.Key summary points-The aim of this work was to evaluate the performance of ChatGPT-4 and ChatGPT-3.5 for providing the provisional diagnosis of different corneal eye diseases based on case descriptions and compared them with three cornea specialists.-The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was significantly better than ChatGPT-3.5 based on the specific cases.-The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% while the interobserver agreement between ChatGPT-4.0 and three cornea specialists were 85%, 80%, and 75%, respectively.

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A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning

Cited by 16 publications

References 27 publications

Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach

Keratoconus disease classification with multimodel fusion and vision transformer: a pretrained model approach

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

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases

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