Canine impaction classification from panoramic dental radiographic images using deep learning models

Aljabri, Malak; Aljameel, Sumayh S.; Min‐Allah, Nasro; Alhuthyfi, Jwaher; Alghamdi, Leena; Alduhailan, Nouf; Alfehaid, Reem; Alqarawi, Reem; Alhareky, Muhanad; Shahin, Suliman; Turki, Walaa Al

doi:10.1016/j.imu.2022.100918

Cited by 20 publications

(27 citation statements)

References 18 publications

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“…From this database, 65% of images are applied for learning and the residual 35% are applied for testing. Also, a comparative analysis is presented for proposed and existing models: HGC-CNN [23], SVM-ECOC [27], VGG16 [28], VGG19 [28], DenseNet121 [30], InceptionV3 [30], ResNet50 [30], YOLOv3 [31] and FRCNN [21] regarding precision, recall, f-measure and accuracy. The evaluation metrics are described below.…”

Section: Resultsmentioning

confidence: 99%

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Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

2022

IJIES

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Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative disorder affecting the TMJ and is distinguished by the gradual deterioration of the joint's interior surfaces. To identify and classify the TMJ-OA from panoramic dental X-ray images, many deep learning models were developed. Amongst, a faster region-based convolutional neural network (FRCNN) can find the condylar area and recognize its abnormalities by learning adequate features with a limited number of images. Nonetheless, the accuracy was not effective for larger databases. Hence in this article, an optimized generative adversarial network (OGAN) model is proposed to create larger panoramic dental X-ray images for TMJ-OA recognition. This GAN model utilizes the generator to produce synthetic panoramic dental images and trains the discriminator to decide whether the created images are real or counterfeit. Besides, an Elephant Herding Optimization (EHO) algorithm is adopted to select the most optimal hyperparameters of the GAN according to the clan and separating factors. Then, the created synthetic panoramic images are added to the actual database and partitioned into 2 distinct collections: training and test collections. The training collection is utilized to train the FRCNN, which extracts the condylar area from every image and identifies the abnormalities. Further, the trained model is tested using the test set to analyze the efficiency of TMJ-OA recognition. Finally, the experimental results exhibit that the OGAN-FRCNN model achieves an accuracy of 94.59% on the panoramic dental X-ray database, whereas the classical models such as VGG16,

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Section: Resultsmentioning

confidence: 99%

“…A range of deep learning structures like DenseNet121, VGG16, InceptionV3 and ResNet50 was recommended [30] to categorize the kind of canine impaction from panoramic dental radiographic images. But, the major drawback of this study was the limited dimension of the annotated database.…”

Section: Literature Surveymentioning

confidence: 99%

Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

2022

IJIES

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“…Majority of the studies suggest that the canine impaction prevalence was found to be more in females. [ 35 ] Some studies, however, have indicated equal frequency of impacted canines in both genders. [ 36 ] We also discovered that male and female individuals had about equal prevalence in the current investigation.…”

Section: Resultsmentioning

confidence: 99%

Association of vertical growth pattern with canine impactions in Dravidian subjects

Bharathi

Jain

Prasad

2022

J Adv Pharm Technol Res

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“…Deep learning is a subfield of machine learning that is part of artificial intelligence. [31]. Deep learning consists of several artificial neural networks that are interconnected [26], [30].…”

Section: Overview Deep Learning Algorithmmentioning

confidence: 99%

“…Deep learning consists of several artificial neural networks that are interconnected [26], [30]. Deep Learning classification or regression model delivered from the output port [30], [31]. This regression classification or model can be applied to invisible data sets for label attribute prediction [22].…”

Section: Overview Deep Learning Algorithmmentioning

confidence: 99%

Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach

Marleny¹,

Mambang

2022

TIERS

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Floods and abrasion are the most common disasters in Indonesia. A lot of data is collected from post-flood and abrasion disasters. From the data released by BNPB, disaster data is directly based on the occurrence of disasters. From these data, we will test predictive modeling classification with a deep learning approach. Big data can be made through classification and predictive modeling. Our proposed model is a classification of predictive modeling of post-flood and abrasion data using the H2O deep learning approach. Deep learning H2O models can also be evaluated with specific model metrics, termination metrics, and performance charts. This approach is used to optimize the performance and accuracy of predictions during the modeling process using our dataset pool training. The big data to be processed will generate new knowledge for policies in decision making. Big data performance modeled with Deep Learning H2O is used to predict the Survival attributes of post-flood and abrasion sample datasets. The best metric performance is obtained from the maxout activation function with a 200-200 unit layer that gets an accuracy of 93.49% with AUC: 0.808 +/- 0.022 (micro average: 0.808).

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Canine impaction classification from panoramic dental radiographic images using deep learning models

Cited by 20 publications

References 18 publications

Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

Optimized Adversarial Network with Faster Residual Deep Learning for Osteoarthritis Classification in Panoramic Radiography

Association of vertical growth pattern with canine impactions in Dravidian subjects

Predictive Modeling Classification of Post-Flood and Abrasion Effects With Deep Learning Approach

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