Deep Learning in Finance: From Implementation to Regulation

Bertucci, Louis; Brière, Marie; Fliche, Olivier; Mikael, Joseph; Szpruch, Łukasz

doi:10.2139/ssrn.4080171

Cited by 5 publications

(2 citation statements)

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“…Convolutional neural networks (CNN) are a subset of deep learning and are well-suited to image classification [ 14 ]. In several disciplines, including engineering, agriculture, economics, medicine, and dentistry, there has been an increase in recent years in the amount of research based on AI algorithms [ 14 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, they can prevent misdiagnoses and incorrect treatment planning (due to workload, carelessness, or inexperience), as well as an unnecessary loss of time or workload in dentistry [ 19 ]. When the literature was searched, only one study using artificial intelligence-based models to segment the sella turcica was discovered [ 17 ]. In this study on lateral cephalometric radiographs, a U-Net architecture-based model and VGG19, ResNet34, InceptionV3, and ResNext50 transfer learning methods were compared, and it was determined that VGG19 and ResNet34 performed better than InceptionV3 and ResNext50.…”

Section: Introductionmentioning

confidence: 99%

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Convolutional Neural Network Performance for Sella Turcica Segmentation and Classification Using CBCT Images

et al. 2022

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The present study aims to validate the diagnostic performance and evaluate the reliability of an artificial intelligence system based on the convolutional neural network method for the morphological classification of sella turcica in CBCT (cone-beam computed tomography) images. In this retrospective study, sella segmentation and classification models (CranioCatch, Eskisehir, Türkiye) were applied to sagittal slices of CBCT images, using PyTorch supported by U-Net and TensorFlow 1, and we implemented the GoogleNet Inception V3 algorithm. The AI models achieved successful results for sella turcica segmentation of CBCT images based on the deep learning models. The sensitivity, precision, and F-measure values were 1.0, 1.0, and 1.0, respectively, for segmentation of sella turcica in sagittal slices of CBCT images. The sensitivity, precision, accuracy, and F1-score were 1.0, 0.95, 0.98, and 0.84, respectively, for sella-turcica-flattened classification; 0.95, 0.83, 0.92, and 0.88, respectively, for sella-turcica-oval classification; 0.75, 0.94, 0.90, and 0.83, respectively, for sella-turcica-round classification. It is predicted that detecting anatomical landmarks with orthodontic importance, such as the sella point, with artificial intelligence algorithms will save time for orthodontists and facilitate diagnosis.

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