Advances in the use of deep learning for the analysis of magnetic resonance image in neuro-oncology

C Pitarch, G Ungan, M Julià-Sapé, A Vellido - Cancers, 2024‏ - mdpi.com
Simple Summary Within the rapidly evolving landscape of Machine Learning in the medical
field, this paper focuses on the forefront advancements in neuro-oncological radiology. More …

[HTML][HTML] The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection

T Berghout - Journal of Imaging, 2024‏ - mdpi.com
Brain tumor detection is crucial in medical research due to high mortality rates and treatment
challenges. Early and accurate diagnosis is vital for improving patient outcomes, however …

Expert Model Prediction Through Feature Matching

B Paudel, R Zwiggelaar, O Akanyeti - Annual Conference on Medical …, 2024‏ - Springer
Supervised brain MRI segmentation performance relies on test sample alignment to the
training domain. This is a function of various factors outside practical control such as …

Classification of LGG/GBM Brain Tumor in MRI Using Deep-Learning Schemes: A Study

S Yamuna, K Vijayakumar… - … Conference on System …, 2023‏ - ieeexplore.ieee.org
Brain abnormalities require immediate medical attention, including diagnosis and treatment.
One of the most severe brain disorders is brain tumor, and magnetic resonance imaging …

Interactive decision support system for lung cancer segmentation

V Sydorskyi - arxiv preprint arxiv:2408.14521, 2024‏ - arxiv.org
This paper studies Clinical Intelligent Decision Support Systems (CIDSSs) for lung cancer
segmentation, which are based on deep neural nets. A new interactive CIDSS is proposed …