Szczegóły publikacji
Opis bibliograficzny
Deep learning and cloud-based computation for cervical spine fracture detection system / Paweł Chłąd, Marek R. OGIELA // Electronics [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 2079-9292. — 2023 — vol. 12 iss. 9 art. no. 2056, s. 1–16. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 15–16, Abstr. — Publikacja dostępna online od: 2023-04-29
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 148583 |
|---|---|
| Data dodania do BaDAP | 2023-10-17 |
| Tekst źródłowy | URL |
| DOI | 10.3390/electronics12092056 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Electronics |
Abstract
Modern machine learning models, such as vision transformers (ViT), have been shown to outperform convolutional neural networks (CNNs) while using fewer computational resources. Although computed tomography (CT) is now the standard for imaging diagnosis of adult spine fractures, analyzing CT scans by hand is both time consuming and error prone. Deep learning (DL) techniques can offer more effective methods for detecting fractures, and with the increasing availability of ubiquitous cloud resources, implementing such systems worldwide is becoming more feasible. This study aims to evaluate the effectiveness of ViT for detecting cervical spine fractures. Data gathered during the research indicates that ViT models are suitable for large-scale automatic detection system implementation. The model achieved 98% accuracy and was easy to train while also being easily explainable.