ArticleDiagnostics (Basel, Switzerland)2023
Blockchain-Based Deep CNN for Brain Tumor Prediction Using MRI Scans.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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
7 citing papers in PubMed.
- Advanced Deep Learning Architectures in MRI-Based Brain Tumor Classification: A Systematic Review Focused on Meningiomas.Journal of imaging informatics in medicine · 2026Review
- Deep learning-driven brain tumor classification and segmentation using non-contrast MRI.Scientific reports · 2025Article
- Lightweight CNN for accurate brain tumor detection from MRI with limited training data.Frontiers in medicine · 2025Article
- Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure.Advances in respiratory medicine · 2024Article
- Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization.Scientific reports · 2024Article
- Efficient Skip Connections-Based Residual Network (ESRNet) for Brain Tumor Classification.Diagnostics (Basel, Switzerland) · 2023Article
- A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment.Frontiers in genetics · 2023Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.
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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.