ArticleJournal of ambient intelligence and humanized computing2023
MCNN: a multi-level CNN model for the classification of brain tumors in IoT-healthcare system.
Article in Journal of ambient intelligence and humanized computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 66 citations in OpenAlex.
- A minimal-net CNN model for an IoT-based brain tumor detection and monitoring system.Scientific reports · 2026Article
- BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.Brain informatics · 2026Article
- Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems.Scientific reports · 2025Article
- A hybrid approach with metaheuristic optimization and random forest in improving heart disease prediction.Scientific reports · 2025Article
- Learning Architecture for Brain Tumor Classification Based on Deep Convolutional Neural Network: Classic and ResNet50.Diagnostics (Basel, Switzerland) · 2025Article
- Context aware machine learning techniques for brain tumor classification and detection - A review.Heliyon · 2025Review
- Revolutionizing heart disease prediction with quantum-enhanced machine learning.Scientific reports · 2024Article
- DDFC: deep learning approach for deep feature extraction and classification of brain tumors using magnetic resonance imaging in E-healthcare system.Scientific reports · 2024Article
- Convolutional neural network based data interpretable framework for Alzheimer's treatment planning.Visual computing for industry, biomedicine, and art · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture.Behavioural neurology · 2024Article
- Cardiac disease prediction using AI algorithms with SelectKBest.Medical & biological engineering & computing · 2023Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The classification of brain tumors is significantly important for diagnosing and treating brain tumors in IoT healthcare systems. In this work, we have proposed a robust classification model for brain tumors employing deep learning techniques. In the design of the proposed method, an improved Convolutional neural network is used to classify Meningioma, Glioma, and Pituitary types of brain tumors. To test the multi-level convolutional neural network model, brain magnetic resonance image data is utilized. The MCNN model classification results were improved using data augmentation and transfer learning methods. In addition, hold-out and performance evaluation metrics have been employed in the proposed MCNN model. The experimental results show that the proposed model obtained higher outcomes than the state-of-the-art techniques and achieved 99.89% classification accuracy. Due to the higher results of the proposed approach, we recommend it for the identification of brain cancer in IoT-healthcare systems.
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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.