Effective Factor Detection in Crowdfunding Systems

Atagün, Ercan; Yildiz, Tuba Karagül; Timuçin, Tunahan; Gündüz, Hakan; Bayıroğlu, Hacer

doi:10.1007/978-3-030-79357-9_25

Cited by 1 publication

(1 citation statement)

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“…Also, time constraints and other commitments are considered. Convolutional networks (CNNs), occupying state of the art for various tasks in computer vision, have proven to be successful for a wide range of applications, including image classification [ 14 , 18 ], image segmentation, the alignment of images [ 16 ], the detection of facial points [ 4 ], the estimation of human postures [ 21 ], and the detection of lines on roads [ 11 , 12 ], among other tasks. Currently, it is in the field of medicine that a great trend has been seen in the use of CNN to automate the process of detecting and diagnosing diseases [ 6 , 17 , 7 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

Automation of Cephalometrics Using Machine Learning Methods

Alshamrani

Alqahtani

Alshehri

2022

Computational Intelligence and Neuroscience

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Cephalometry is a medical test that can detect teeth, skeleton, or appearance problems. In this scenario, the patient’s lateral radiograph of the face was utilised to construct a tracing from the tracing of lines on the lateral radiograph of the face of the soft and hard structures (skin and bone, respectively). Certain cephalometric locations and characteristic lines and angles are indicated after the tracing is completed to do the real examination. In this unique study, it is proposed that machine learning models be employed to create cephalometry. These models can recognise cephalometric locations in X-ray images, allowing the study’s computing procedure to be completed faster. To correlate a probability map with an input image, they combine an Autoencoder architecture with convolutional neural networks and Inception layers. These innovative architectures were demonstrated. When many models were compared, it was observed that they all performed admirably in this task.

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