Deep neural network correlation learning mechanism for CT brain tumor detection
importance 5/10
ai assisted
Brain Tumor Detection and Classification neuro-oncology imaging stage: research
Problem
Detecting brain tumors in CT scans accurately and efficiently to support clinical diagnosis.
Why it matters
Accurate early tumor detection directly affects patient treatment and outcomes.
What the AI did
Proposed a correlation learning mechanism (CLM) where a support neural network helps a CNN find adequate filters, making the classifier learn faster and reach higher efficiency.
Results
About 96% accuracy, about 95% precision and recall.
Impact
Authors report improved CT brain tumor detection accuracy over standard approaches.
Record
| Venue | Neural Computing and Applications |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Marcin Woźniak, Jakub Siłka, Michał Wieczorek |
| Citations | 284 (OpenAlex, at import time) |
| References | 50 |
| Model or method | CNN with correlation learning mechanism (CLM) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1007/s00521-021-05841-x |
| Open access | hybrid |
| Keywords | CT scans, brain tumor, CNN, deep learning, medical imaging, detection |
Importance rating
5
real but narrower advance
Brain tumor detection in CT affects patient treatment and outcomes, a real clinical advance, but abstract presents a single detection task without stated scale or deployment.