ArticleHeliyon2024
Brain tumor diagnosis from MRI based on Mobilenetv2 optimized by contracted fox optimization algorithm.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.Journal of imaging informatics in medicine · 2025Pooled it
- Advances in AI-based diagnosis of Alzheimer's disease using MRI: a comprehensive survey.Frontiers in medicine · 2026Review
- Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.Frontiers in neuroscience · 2026Review
- Enhanced image registration based brain tumour segmentation using optical particle swarm intelligence technique with Resnet Inceptionv2 HCNN.Scientific reports · 2025Article
- Applying machine learning with MobileNetV2 model for rapid screening of vaginal discharge samples in vaginitis diagnosis.Scientific reports · 2025Article
- Real-Time Object Detector for Medical Diagnostics (RTMDet): A High-Performance Deep Learning Model for Brain Tumor Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms.Brain sciences · 2024Article
- An efficient brain tumor detection and classification using pre-trained convolutional neural network models.Heliyon · 2024Article
- FOX Optimization Algorithm Based on Adaptive Spiral Flight and Multi-Strategy Fusion.Biomimetics (Basel, Switzerland) · 2024Article
- A Novel Ensemble Framework for Multi-Classification of Brain Tumors Using Magnetic Resonance Imaging.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning pipeline reveals key moments in human embryonic development predictive of live birth after in vitro fertilization.Biology methods & protocols · 2024Article
Corrections and comments
- Retraction · 2026-01-22Author Unresponsive · Concerns/Issues about Referencing/Attributions · Investigation by Journal/Publisher · Unreliable Results and/or Conclusions ·
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research paper presents an innovative approach to brain tumor diagnosis using MRI scans, using the power of deep learning and metaheuristic algorithm. The study employs Mobilenetv2, a deep learning model, optimized by a novel metaheuristic known as the Contracted Fox Optimization Algorithm (MN-V2/CFO). This methodology allows for the optimal selection of Mobilenetv2 hyperparameters, enhancing the accuracy of tumor detection. The model is implemented on the Figshare dataset, a comprehensive collection of MRI scans, and its performance is validated against other processes the results are compared with some published works including Network (RN), wavelet transform, and deep learning (WT/DL), customized VGG19, and Convolutional neural network (CNN). The results of the study, highlight the superior performance of the proposed MN-V2/CFO model compared to other tactics. The recommended strategy achieves a precision of 97.68 %, an F1-score of 86.22 %, a sensitivity of 80.12 %, and an accuracy of 97.32 %. The findings validate the potential of the proposed model in revolutionizing brain tumor diagnosis, contributing to better treatment strategies, and improving patient outcomes.
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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.