ReviewSensors (Basel, Switzerland)2022
Deep Learning for Smart Healthcare-A Survey on Brain Tumor Detection from Medical Imaging.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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Who cites it
39 citing papers in PubMed, 236 citations in OpenAlex.
- Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.Scientific reports · 2026Article
- High-precision classification of WCE-based gastrointestinal abnormality using a fusion deep learning approach.Scientific reports · 2026Article
- Hyperparameter Optimization of Convolutional Neural Networks for Robust Tumor Image Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Adaptive multi-feature fusion architecture with optimized learning for high-fidelity brain tumor classification in MRI.Scientific reports · 2026Article
- Advancing Medical Decision-Making with AI: A Comprehensive Exploration of the Evolution from Convolutional Neural Networks to Capsule Networks.Journal of imaging · 2025Review
- Smart IoT with the hybrid evolutionary method and image processing for tumor detection.Scientific reports · 2025Article
- Enhanced MRI brain tumor detection using deep learning in conjunction with explainable AI SHAP based diverse and multi feature analysis.Scientific reports · 2025Article
- Artificial Intelligence Approaches for Geographic Atrophy Segmentation: A Systematic Review and Meta-Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Interactive Multi-scale Fusion: Advancing Brain Tumor Detection Through Trans-IMSM Model.Journal of imaging informatics in medicine · 2025Article
- Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Improving healthcare sustainability using advanced brain simulations using a multi-modal deep learning strategy with VGG19 and bidirectional LSTM.Frontiers in medicine · 2025Article
- Development of brain tumor radiogenomic classification using GAN-based augmentation of MRI slices in the newly released gazi brains dataset.BMC medical informatics and decision making · 2024Article
- Feature-based detection of breast cancer using convolutional neural network and feature engineering.Scientific reports · 2024Article
- Brain Tumor MRI Classification Using a Novel Deep Residual and Regional CNN.Biomedicines · 2024Article
- Age-appropriate design of smart senior care product APP interface based on deep learning.Heliyon · 2024Article
- Enhanced MRI-based brain tumour classification with a novel Pix2pix generative adversarial network augmentation framework.Brain communications · 2024Article
- A big data analysis algorithm for massive sensor medical images.PeerJ. Computer science · 2024Article
- Transfer learning with CNNs for efficient prostate cancer and BPH detection in transrectal ultrasound images.Scientific reports · 2023Article
- Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.Cancers · 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
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Advances in technology have been able to affect all aspects of human life. For example, the use of technology in medicine has made significant contributions to human society. In this article, we focus on technology assistance for one of the most common and deadly diseases to exist, which is brain tumors. Every year, many people die due to brain tumors; based on "braintumor" website estimation in the U.S., about 700,000 people have primary brain tumors, and about 85,000 people are added to this estimation every year. To solve this problem, artificial intelligence has come to the aid of medicine and humans. Magnetic resonance imaging (MRI) is the most common method to diagnose brain tumors. Additionally, MRI is commonly used in medical imaging and image processing to diagnose dissimilarity in different parts of the body. In this study, we conducted a comprehensive review on the existing efforts for applying different types of deep learning methods on the MRI data and determined the existing challenges in the domain followed by potential future directions. One of the branches of deep learning that has been very successful in processing medical images is CNN. Therefore, in this survey, various architectures of CNN were reviewed with a focus on the processing of medical images, especially brain MRI images.
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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.