ArticleComputational and mathematical methods in medicine2022
Diagnosis of Brain Tumor Using Light Weight Deep Learning Model with Fine-Tuning Approach.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 73 citations in OpenAlex.
- Altitudinal Visual Field Defect as the Presenting Sign of a Brain Tumor: A Case Report.Clinical case reports · 2026Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- A deep learning approach for brain tumour classification and detection in MRI images using YOLOv7.Frontiers in oncology · 2025Article
- Lightweight-CancerNet: a deep learning approach for brain tumor detection.PeerJ. Computer science · 2025Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.Cureus · 2024Article
- A hybrid deep CNN model for brain tumor image multi-classification.BMC medical imaging · 2024Article
- A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.Biomedical engineering and computational biology · 2024Article
- A deep learning model for the localization and extraction of brain tumors from MR images using YOLOv7 and grab cut algorithm.Frontiers in oncology · 2024Article
- An Augmented Modulated Deep Learning Based Intelligent Predictive Model for Brain Tumor Detection Using GAN Ensemble.Sensors (Basel, Switzerland) · 2023Article
- In-Domain Transfer Learning Strategy for Tumor Detection on Brain MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Brain Tumor Class Detection in Flair/T2 Modality MRI Slices Using Elephant-Herd Algorithm Optimized Features.Diagnostics (Basel, Switzerland) · 2023Article
- A schizophrenia study based on multi-frequency dynamic functional connectivity analysis of fMRI.Frontiers in human neuroscience · 2023Article
- Racial differences in prevalence and anatomical distribution of tarsal coalition.Scientific reports · 2022Article
- The Reform of University Education Teaching Based on Cloud Computing and Big Data Background.Computational intelligence and neuroscience · 2022Article
- Association between the Systolic Blood Pressure Trajectory and Risk of Stroke in a Health-Management Population in Jiaozuo, China.Journal of healthcare engineering · 2022Article
- Effects of Data Augmentation with the BNNSMOTE Algorithm in Seizure Detection Using 1D-MobileNet.Journal of healthcare engineering · 2022Article
- Association between Cardio-Ankle Vascular Index and Masked Uncontrolled Hypertension in Hypertensive Patients: A Cross-Sectional Study.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain cancer is a rare and deadly disease with a slim chance of survival. One of the most important tasks for neurologists and radiologists is to detect brain tumors early. Recent claims have been made that computer-aided diagnosis-based systems can diagnose brain tumors by employing magnetic resonance imaging (MRI) as a supporting technology. We propose transfer learning approaches for a deep learning model to detect malignant tumors, such as glioblastoma, using MRI scans in this study. This paper presents a deep learning-based approach for brain tumor identification and classification using the state-of-the-art object detection framework YOLO (You Only Look Once). The YOLOv5 is a novel object detection deep learning technique that requires limited computational architecture than its competing models. The study used the Brats 2021 dataset from the RSNA-MICCAI brain tumor radio genomic classification. The dataset has images annotated from RSNA-MICCAI brain tumor radio genomic competition dataset using the make sense an AI online tool for labeling dataset. The preprocessed data is then divided into testing and training for the model. The YOLOv5 model provides a precision of 88 percent. Finally, our model is tested across the whole dataset, and it is concluded that it is able to detect brain tumors successfully.
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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.