Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques
519
Zitationen
4
Autoren
2023
Jahr
Abstract
The present study shows that the proposed 2D CNN has optimal accuracy in classifying brain tumors. Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN achieved exemplary performance and optimal execution time without latency. This proposed network is less complex than the auto-encoder network and can be employed by radiologists and physicians in clinical systems for brain tumor detection.
Ähnliche Arbeiten
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
2018 · 6.429 Zit.
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2014 · 6.357 Zit.
A Comprehensive Survey on Graph Neural Networks
2021 · 3.310 Zit.
Brain tumor segmentation with Deep Neural Networks
2016 · 3.204 Zit.
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
2016 · 2.630 Zit.