Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs

Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.

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Deep learning neural networks to differentiate Stafne’s bone cavity from pathological radiolucent lesions of the mandible in heterogeneous panoramic radiography

PLoS ONE ◽

10.1371/journal.pone.0254997 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254997

Author(s):

Ari Lee ◽

Min Su Kim ◽

Sang-Sun Han ◽

PooGyeon Park ◽

Chena Lee ◽

...

Keyword(s):

Deep Learning ◽

High Performance ◽

Imaging System ◽

Panoramic Radiography ◽

Learning Algorithm ◽

Odontogenic Cysts ◽

Surgical Removal ◽

Imaging Features ◽

Deep Learning Algorithm ◽

Dentigerous Cysts

This study aimed to develop a high-performance deep learning algorithm to differentiate Stafne’s bone cavity (SBC) from cysts and tumors of the jaw based on images acquired from various panoramic radiographic systems. Data sets included 176 Stafne’s bone cavities and 282 odontogenic cysts and tumors of the mandible (98 dentigerous cysts, 91 odontogenic keratocysts, and 93 ameloblastomas) that required surgical removal. Panoramic radiographs were obtained using three different imaging systems. The trained model showed 99.25% accuracy, 98.08% sensitivity, and 100% specificity for SBC classification and resulted in one misclassified SBC case. The algorithm was approved to recognize the typical imaging features of SBC in panoramic radiography regardless of the imaging system when traced back with Grad-Cam and Guided Grad-Cam methods. The deep learning model for SBC differentiating from odontogenic cysts and tumors showed high performance with images obtained from multiple panoramic systems. The present algorithm is expected to be a useful tool for clinicians, as it diagnoses SBCs in panoramic radiography to prevent unnecessary examinations for patients. Additionally, it would provide support for clinicians to determine further examinations or referrals to surgeons for cases where even experts are unsure of diagnosis using panoramic radiography alone.

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Early-Stage Detection of Cancer in Breast Using Artificial Intelligence

Revista Gestão Inovação e Tecnologias ◽

10.47059/revistageintec.v11i2.1821 ◽

2021 ◽

Vol 11 (2) ◽

pp. 2016-2028

Author(s):

M.N. Vimal Kumar ◽

S. Aakash Ram ◽

C. Shobana Nageswari ◽

C. Raveena ◽

S. Rajan

Keyword(s):

Breast Cancer ◽

Artificial Intelligence ◽

Deep Learning ◽

Learning Algorithm ◽

Early Stage ◽

Deep Learning Algorithm ◽

Common Cancer ◽

Abnormal Cells ◽

Detection And Diagnosis ◽

Early Detection And Diagnosis

One of the deadly diseases among humans is Cancer, which occurs almost anywhere in the human body. Cancer is caused by the cells that spread into the surrounding tissues by dividing itself uncontrollably. Breast Cancer is the most common cancer among women. Early detection and diagnosis of breast cancer are treatable and curable. Many women have no symptoms for this cancer at an early stage. The abnormal cells in the breast will risk for the development of breast cancer. So, it is important for women to regularly examine their breast. Technologies can be utilized in a smarter way with Artificial Intelligence techniques to assist the women during their examination of the breast at their living place to avoid the risk of breast cancer. The main aim is to develop a lowcost self-examining device for the detection of breast cancer and abnormality in the breast using an efficient optical method, Deep-learning algorithm and Internet of Things.

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Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

Diagnostics ◽

10.3390/diagnostics10060430 ◽

2020 ◽

Vol 10 (6) ◽

pp. 430 ◽

Cited By ~ 1

Author(s):

Michael G. Endres ◽

Florian Hillen ◽

Marios Salloumis ◽

Ahmad R. Sedaghat ◽

Stefan M. Niehues ◽

...

Keyword(s):

Deep Learning ◽

Learning Algorithm ◽

Ground Truth ◽

True Positive Rate ◽

Radiographic Images ◽

Data Set ◽

Radiographic Findings ◽

Panoramic Radiographs ◽

Deep Learning Algorithm ◽

The Mean

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69 (±0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51 (±0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60 (±0.04), and an F1 score of 0.58 (±0.04) corresponding to a PPV of 0.67 (±0.05) and TPR of 0.51 (±0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs.

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Automatic Car Damage detection by Hybrid Deep Learning Multi Label Classification

Journal of Artificial Intelligence and Capsule Networks - September 2019 ◽

10.36548/jaicn.2021.4.006 ◽

2021 ◽

Vol 3 (4) ◽

pp. 341-352

Author(s):

P. Ebby Darney

Keyword(s):

Deep Learning ◽

Damage Detection ◽

Learning Algorithm ◽

Research Work ◽

Computation Time ◽

Damage Classification ◽

Neural Network Approach ◽

Convolutional Networks ◽

Deep Learning Algorithm ◽

Car Insurance

Automating image-based automobile insurance claims processing is a significant opportunity. In this research work, car damage categorization that is aided by the hybrid convolutional neural network approach is addressed and hence the deep learning-based strategies are applied. Insurance firms may leverage this paper's design and implementation of an automobile damage classification/detection pipeline to streamline car insurance claim policy. Using deep convolutional networks to detect car damage is now possible because of recent improvements in the artificial intelligence sector, mainly due to less computation time and higher accuracy with a hybrid transformation deep learning algorithm. In this paper, multiclass classification proposed to categorize the car damage parts such as broken headlight/taillight, glass fragments, damaged bonnet etc. are compiled into the proposed dataset. This model has been pre-trained on a wide-ranging and benchmark dataset due to the dataset's limited size to minimize overfitting and to understand more common properties of the dataset. To increase the overall proposed model’s performance, the CNN feature extraction model is trained with Resnet architecture with the coco car damage detection datasets and reaches a higher accuracy of 90.82%, which is much better than the previous findings on the comparable test sets.

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