ArticleSensors (Basel, Switzerland)2022
Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age.
Article 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 16 papers.
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Who cites it
16 citing papers in PubMed.
- SScientific reports · 2026Article
- Automated Multi-grade Brain Tumor Classification Using Adaptive Hierarchical Optimized Horse Herd BiLSTM Fusion Network in MRI Images.Interdisciplinary sciences, computational life sciences · 2026Article
- Automated Brain Tumor Classification and Grading Using Multi-scale Graph Neural Network with Spatio-Temporal Transformer Attention Through MRI Scans.Interdisciplinary sciences, computational life sciences · 2026Article
- Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification.PloS one · 2026Article
- Machine Learning in MRI Brain Imaging: A Review of Methods, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Let UNet Play an Adversarial Game: Investigating the Effect of Adversarial Training in Enhancing Low-Resolution MRI.Journal of imaging informatics in medicine · 2025Article
- A novel brain tumor magnetic resonance imaging dataset (Gazi Brains 2020): initial benchmark results and comprehensive analysis.PeerJ. Computer science · 2025Article
- Article
- Medical image analysis using improved SAM-Med2D: segmentation and classification perspectives.BMC medical imaging · 2024Article
- Brain Tumor Detection and Classification Using an Optimized Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2024Article
- Learning to deep learning: statistics and a paradigm test in selecting a UNet architecture to enhance MRI.Magma (New York, N.Y.) · 2024Article
- Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images.Zeitschrift fur medizinische Physik · 2024Article
- Long short-term memory models of water quality in inland water environments.Water research X · 2023Review
- Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning.ACS omega · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- An Enhanced Machine Learning Approach for Brain MRI Classification.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.
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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.