ArticleComputational intelligence and neuroscience2021
A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network.
Article in Computational intelligence and neuroscience, 2021. 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 It has been retracted, and should not be counted.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 9 papers.
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
9 citing papers in PubMed, 85 citations in OpenAlex.
- Automatic smart brain tumor classification and prediction system using deep learning.Scientific reports · 2025Article
- Enhancing multiclass brain tumor diagnosis using SVM and innovative feature extraction techniques.Scientific reports · 2024Article
- Automatic brain-tumor diagnosis using cascaded deep convolutional neural networks with symmetric U-Net and asymmetric residual-blocks.Scientific reports · 2024Article
- A novel approach to brain tumor detection using K-Means++, SGLDM, ResNet50, and synthetic data augmentation.Frontiers in physiology · 2024Article
- A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.Sensors (Basel, Switzerland) · 2023Article
- Retracted: A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network.Computational intelligence and neuroscience · 2023Article
- XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification.Sensors (Basel, Switzerland) · 2022Article
- Deep Neural Networks for Automatic Flower Species Localization and Recognition.Computational intelligence and neuroscience · 2022Article
- Article
Corrections and comments
- Retraction · 2023-11-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients' quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.
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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.