ArticleDiagnostics (Basel, Switzerland)2022
Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed.
- Multi-attention Mechanism for Enhanced Pseudo-3D Prostate Zonal Segmentation.Journal of imaging informatics in medicine · 2025Article
- Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT.Sensors (Basel, Switzerland) · 2025Article
- Brain tumor classification using GAN-augmented data with autoencoders and Swin Transformers.Frontiers in medicine · 2025Article
- The Neural Frontier of Future Medical Imaging: A Review of Deep Learning for Brain Tumor Detection.Journal of imaging · 2024Review
- NAC1 transcriptional activation of LDHA induces hepatitis B virus immune evasion leading to cirrhosis and hepatocellular carcinoma development.Oncogenesis · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- A deep learning model integrating multisequence MRI to predict EGFR mutation subtype in brain metastases from non-small cell lung cancer.European radiology experimental · 2024Article
- Brain Tumor Classification from MRI Using Image Enhancement and Convolutional Neural Network Techniques.Brain sciences · 2023Article
- An Approach to Binary Classification of Alzheimer's Disease Using LSTM.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
In recent years, deep learning has been applied to many medical imaging fields, including medical image processing, bioinformatics, medical image classification, segmentation, and prediction tasks. Computer-aided detection systems have been widely adopted in brain tumor classification, prediction, detection, diagnosis, and segmentation tasks. This work proposes a novel model that combines the Bayesian algorithm with depth-wise separable convolutions for accurate classification and predictions of brain tumors. We combine Bayesian modeling learning and Convolutional Neural Network learning methods for accurate prediction results to provide the radiologists the means to classify the Magnetic Resonance Imaging (MRI) images rapidly. After thorough experimental analysis, our proposed model outperforms other state-of-the-art models in terms of validation accuracy, training accuracy,
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.