Smart Odontogram: Dental Diagnosis of Patients Using Deep Learning

Fadhillah, Excel Daris; Bramastagiri, Pravasta Caraka; Sigit, Riyanto; Sukaridhoto, Sritrusta; Brahmanta, Arya; Dewantara, Bima Sena Bayu

doi:10.1109/ies53407.2021.9594027

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…Recently, intra-oral dental images are used for tooth disease diagnosis. They provide valuable insights into a patient's oral health status and help in formulating treatment plans [9]. This approach (i) does not necessitate specialized equipment for data acquisition, (ii) offer rich features despite small image size, and (iii) consequently requires low computational cost for image processing and object detection tasks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

AbuSalim¹,

Zakaria²,

Mostafa³

et al. 2023

IJACSA

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In image classification, multi-granularity refers to the ability to classify images with different levels of detail or resolution. This is a challenging task because the distinction between subcategories is often minimal, needing a high level of visual detail and precise representation of the features specific to each class. In dental informatics, and more specifically tooth classification poses many challenges due to overlapping teeth, varying sizes, shapes, and illumination levels. To address these issues, this paper considers various data granularity levels since a deeper level of details can be acquired with increased granularity. Three tooth granularity levels are considered in this study named Two Classes Granularity Level (2CGL), Four Classes Granularity Level (4CGL), and Seven Classes Granularity Level (7CGL) to analyze the performance of teeth detection and classification at multi-granularity levels in Granular Intra-Oral Image (GIOI) dataset. Subsequently, a Faster Region-Convolutional Neural Network (FR-CNN) based on three ResNet models is proposed for teeth detection and classification at multi-granularity levels from the GIOI dataset. The FR-CNN-ResNet models exploit the effect of the tooth classification granularity technique to empower the models with accurate features that lead to improved model performance. The results indicate a remarkable detection effect in investigating the granularity effect on the FR-CNN-ResNet model's performance. The FR-CNN-ResNet-50 model achieved 0.94 mAP for 2CGL, 0.74 mAP for 4CGL, and 0.69 mAP for 7CGL, respectively. The findings demonstrated that multi-granularity enables flexible and nuanced analysis of visual data, which can be useful in a wide range of applications.

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Section: Introductionmentioning

confidence: 99%

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

AbuSalim¹,

Zakaria²,

Mostafa³

et al. 2023

IJACSA

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“…Pada praktik kedokteran gigi, pemeriksaan dan perekaman rekam medis masih dilakukan secara manual. Akibatnya, banyak kasus kurangnya kelengkapan pengisian rekam medis karena waktu yang dibutuhkan cukup lama untuk mengevaluasi dan mengisi rekam medis [1] [2].…”

Section: A Pendahuluanunclassified

Deteksi Kondisi Gigi Manusia pada Citra Intraoral Menggunakan YOLOv5

Makarim,

Karlita,

Sigit

et al. 2023

ijcs

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Proses identifikasi dan pencatatan rekam medis pada praktik kedokteran gigi masih dilakukan secara manual. Akibatnya, proses tersebut memakan waktu yang cukup lama. Pada penelitian ini metode deteksi objek dimanfaatkan untuk membantu dokter melakukan identifikasi pada gigi pasien. YOLOv5 dipilih untuk dilatihkan pada dataset citra intraoral dengan lima kelas kondisi gigi (normal, karies, tumpatan, sisa akar, dan impaksi). Dataset yang digunakan berjumlah 1.767 data citra intraoral yang diambil dan dilabeli oleh dokter gigi. Dataset dibagi menjadi tiga bagian, 10% digunakan untuk data testing dan 90% digunakan untuk data training dan validation. Dilakukan komparasi performa berdasarkan nilai metrik evaluasi terhadap tiga jenis model YOLOv5 (S, M, L). Dari hasil pelatihan, YOLOv5 M sebagai model terbaik mendapatkan nilai mAP sebesar 84%, dan 82% nilai akurasi testing. Penelitian ini telah memenuhi tujuan utama untuk membangun sebuah model deep learning yang robust untuk mendeteksi dan mengklasifikasi beberapa kondisi gigi pada manusia.

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“…YOLO, as an object detection neural network, can be used to detect any custom object [15]. YOLO is popular, well known to be a capable object detector, and easily compatible for integration thus, many use it for object detection in their systems [16] [17]. Moreover, the model is very efficient in the sense that it has low computation time with comparable accuracy [18].…”

Section: Introductionmentioning

confidence: 99%

Mangrove Tree Density Detector using YOLO Based on Darknet Framework using RGB Drone Imagery

Yudhistira Kurniawan,

Udin Harun Al Rasyid,

Sukaridhoto

2024

IJCDS

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Mangrove preservation is crucial due to their ecological significance impact. Monitoring the health of mangrove forests is essential for preservation strategy, yet it remains challenging and time-intensive, particularly in remote locations. This study aims to create system to automatically assess mangrove density, providing essential data for informed preservation strategies, such as prioritizing reforestation in low density area. Using drones with RGB cameras to capture aerial imagery, enabling remote data collection. The system utilizes the YOLO neural network object detector to automatically detect objects, enabling quantity estimation. Experiment shows that YOLO object detector is able to detect mangrove tree accurately with 95% recall, 88.3% IoU, and 22ms processing time. The system uses 'tiny' model variant to provide more efficient accuracy compared to computation resource, making it suitable for deployment on computer with limited resources. In comparison to standard model that improves the recall by 4%, IoU by 2%, but demands six times more processing time. Then it calculate the covered area using camera transformation formula. Finally, it calculates the density for mangrove forest health, synchronized with GPS location. With the resulting data on mangrove density, evaluations of mangrove forest health become much easier, facilitating effective preservation actions, such as reforestation in area with low mangrove density.

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Smart Odontogram: Dental Diagnosis of Patients Using Deep Learning

Cited by 4 publications

References 17 publications

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

Multi-Granularity Tooth Analysis via Faster Region-Convolutional Neural Networks for Effective Tooth Detection and Classification

Deteksi Kondisi Gigi Manusia pada Citra Intraoral Menggunakan YOLOv5

Mangrove Tree Density Detector using YOLO Based on Darknet Framework using RGB Drone Imagery

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