ArticleMedical & biological engineering & computing2023
Automatic brain tumor detection using CNN transfer learning approach.
Article in Medical & biological engineering & computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2025Pooled it
- An Efficient Neuro-framework for Brain Tumor Classification Using a CNN-based Self-supervised Learning Approach with Genetic Optimizations.Current neuropharmacology · 2026Article
- Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization.Scientific reports · 2024Article
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.Frontiers in computational neuroscience · 2024Article
- In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Automatic brain tumor detection is a challenging task as tumors vary in their position, mass, nature, and similarities found between brain lesions and normal tissues. The tumor detection is vital and urgent as it is related to the lifespan of the affected person. Medical experts commonly utilize advanced imaging practices such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound images to decide the presence of abnormal tissues. It is a very time-consuming task to extract the tumor information from the enormous quantity of information produced by MRI volumetric data examination using a manual approach. In manual tumor detection, precise identification of tumor along with its details is a complex task. Henceforth, reliable and automatic detection systems are vital. In this paper, convolutional neural network based automated brain tumor recognition approach is proposed to analyze the MRI images and classify them into tumorous and non-tumorous classes. Various convolutional neutral network architectures like Alexnet, VGG-16, GooGLeNet, and RNN are explored and compared together. The paper focuses on the tuning of the hyperparameters for the two architectures namely Alexnet and VGG-16. Exploratory results on BRATS 2013, BRATS 2015, and OPEN I dataset with 621 images confirmed that the accuracy of 98.67% is achieved using CNN Alexnet for automatic detection of brain tumors while testing on 125 images.
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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.