A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

Ali, Ramzi Ben; Ejbali, Ridha; Zaied, Mourad

doi:10.3844/jcssp.2018.1488.1498

Cited by 10 publications

(5 citation statements)

References 41 publications

(42 reference statements)

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“…Artificial Intelligence in Dentistry DOI: http://dx.doi.org/10.5772/intechopen.111532 AI can provide assistance in recognizing some pathologies such as proximal caries and periapical pathologies that cannot be detected on radiographs due to noise, artifact, and low contrast in images [75]. There are many studies that have achieved high-performance results (86-97% accuracy) in the classification of dental caries on radiographs [75][76][77][78][79]. Different DL-based CNN methods have also been developed to detect dental caries on periapical radiographs [13,80].…”

Section: Restorative Dentistrymentioning

confidence: 99%

Artificial Intelligence in Dentistry

Gokdeniz¹,

Büyüksungur²,

Kolsuz³

2023

Dentistry

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Artificial intelligence (AI) is the branch of computer science dedicated to building systems to perform tasks that normally require human intelligence. AI tries to solve problems and learn similar to humans. The field of AI has experienced phenomenal development and growth over the past two decades; with the latest developments in digitized data collection, machine learning, and computing infrastructure, AI applications are expanding rapidly, especially in areas that are thought to be reserved for experts in their fields. Artificial intelligence has started to take place rapidly in dental clinical applications. The use of artificial intelligence in dentistry has enormous potential to improve patient care and drive major advances in healthcare. AI in dentistry is being researched for various purposes, such as identifying anatomical and pathological structures, diagnosing diseases and predicting treatment results, and selecting materials to be used. Advances in AI offer healthcare benefits, such as reducing postoperative complications, improving quality of life, and reducing the number of unnecessary procedures. It can also play a great helping role for dentists in increasing the accuracy of diagnosis. This chapter aims to explain the current applications and future predictions of artificial intelligence in dentistry, which is one of the most current topics of recent times.

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Section: Restorative Dentistrymentioning

confidence: 99%

Artificial Intelligence in Dentistry

Gokdeniz¹,

Büyüksungur²,

Kolsuz³

2023

Dentistry

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“…The back propagation algorithm was used to construct the weights for the NN classifier in this system. Additionally, Ali et al [25] classified dental X-rays into normal or decayed teeth images. They proposed a system of detecting dental caries and classifying X-ray images using deep NNs.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Alharbi,

AlRugaibah,

Alhasson

et al. 2023

Applied Sciences

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Dental caries is one of the most prevalent and chronic diseases worldwide. Dental X-ray radiography is considered a standard tool and a valuable resource for radiologists to identify dental diseases and problems that are hard to recognize by visual inspection alone. However, the available dental panoramic image datasets are extremely limited and only include a small number of images. U-Net is one of the deep learning networks that are showing promising performance in medical image segmentation. In this work, different U-Net models are applied to dental panoramic X-ray images to detect caries lesions. The Detection, Numbering, and Segmentation Panoramic Images (DNS) dataset, which includes 1500 panoramic X-ray images obtained from Ivisionlab, is used in this experiment. The major objective of this work is to extend the DNS Panoramic Images dataset by detecting the cavities in the panoramic image and generating the binary ground truth of this image to use as the ground truth for the evaluation of models. These ground truths are revised by experts to ensure their robustness and correctness. Firstly, we expand the Panoramic Images (DNS) dataset by detecting the cavities in the panoramic images and generating the images’ binary ground truth. Secondly, we apply U-Net, U-Net++ and U-Net3+ to the expanded DNS dataset to learn the hierarchical features and to enhance the cavity boundary. The results show that U-Net3+ outperforms the other versions of U-Net with 95% in testing accuracy.

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“…AI can also help detect proximal caries and periapical pathologies that are frequently missed by human eyes on radiographs because of picture noise and/or low contrast [18]. Several researches revealed high-performance results in diagnosing dental caries in radiographies using various image processing approaches followed by machine leaning (ML) classifiers [18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs

et al. 2023

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Background Introducing artificial intelligence (AI) into the medical field proved beneficial in automating tasks and streamlining the practitioners’ lives. Hence, this study was conducted to design and evaluate an AI tool called Make Sure Caries Detector and Classifier (MSc) for detecting pathological exposure of pulp on digital periapical radiographs and to compare its performance with dentists. Methods This study was a diagnostic, multi-centric study, with 3461 digital periapical radiographs from three countries and seven centers. MSc was built using Yolov5-x model, and it was used for exposed and unexposed pulp detection. The dataset was split into a train, validate, and test dataset; the ratio was 8–1-1 to prevent overfitting. 345 images with 752 labels were randomly allocated to test MSc. The performance metrics used to test MSc performance included mean average precision (mAP), precision, F1 score, recall, and area under receiver operating characteristic curve (AUC). The metrics used to compare the performance with that of 10 certified dentists were: right diagnosis exposed (RDE), right diagnosis not exposed (RDNE), false diagnosis exposed (FDE), false diagnosis not exposed (FDNE), missed diagnosis (MD), and over diagnosis (OD). Results MSc achieved a performance of more than 90% in all metrics examined: an average precision of 0.928, recall of 0.918, F1-score of 0.922, and AUC of 0.956 (P<.05). The results showed a higher mean of 1.94 for all right (correct) diagnosis parameters in MSc group, while a higher mean of 0.64 for all wrong diagnosis parameters in the dentists group (P<.05). Conclusions The designed MSc tool proved itself reliable in the detection and differentiating between exposed and unexposed pulp in the internally validated model. It also showed a better performance for the detection of exposed and unexposed pulp when compared to the 10 dentists’ consensus.

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A Deep Convolutional Neural Wavelet Network for Classification of Medical Images

Cited by 10 publications

References 41 publications

Artificial Intelligence in Dentistry

Artificial Intelligence in Dentistry

Detection of Cavities from Dental Panoramic X-ray Images Using Nested U-Net Models

Detection of the pathological exposure of pulp using an artificial intelligence tool: a multicentric study over periapical radiographs

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