ArticleBioMed research international2022
Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection.
Article in BioMed research international, 2022. 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 It has been retracted, and should not be counted.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 18 papers.
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Who cites it
18 citing papers in PubMed, 79 citations in OpenAlex.
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- Development of a deep learning radiomics model combining lumbar CT, multi-sequence MRI, and clinical data to predict high-risk cage subsidence after lumbar fusion: a retrospective multicenter study.Biomedical engineering online · 2025Article
- Brain Tumour Detection Using VGG-Based Feature Extraction With Modified DarkNet-53 Model.International journal of biomedical imaging · 2025Article
- Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.Scientific reports · 2024Article
- Development and validation of a predictive model for vertebral fracture risk in osteoporosis patients.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- PlaqueNet: deep learning enabled coronary artery plaque segmentation from coronary computed tomography angiography.Visual computing for industry, biomedicine, and art · 2024Article
- Marker-controlled watershed algorithm and fuzzy C-means clustering machine learning: automated segmentation of glioblastoma from MRI images in a case series.Annals of medicine and surgery (2012) · 2024Article
- Exploring deep learning radiomics for classifying osteoporotic vertebral fractures in X-ray images.Frontiers in endocrinology · 2024Article
- Brain MRI detection and classification: Harnessing convolutional neural networks and multi-level thresholding.PloS one · 2024Article
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- Mu-Net a Light Architecture for Small Dataset Segmentation of Brain Organoid Bright-Field Images.Biomedicines · 2023Article
- Enhancing Prediction of Brain Tumor Classification Using Images and Numerical Data Features.Diagnostics (Basel, Switzerland) · 2023Article
- Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features.BMC musculoskeletal disorders · 2023Article
- A Novel Lightweight CNN Architecture for the Diagnosis of Brain Tumors Using MR Images.Diagnostics (Basel, Switzerland) · 2023Article
- Retracted: Enhanced Watershed Segmentation Algorithm-Based Modified ResNet50 Model for Brain Tumor Detection.BioMed research international · 2023Article
- XDecompo: Explainable Decomposition Approach in Convolutional Neural Networks for Tumour Image Classification.Sensors (Basel, Switzerland) · 2022Article
- 3D Input Convolutional Neural Network for SSVEP Classification in Design of Brain Computer Interface for Patient User.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
- Retraction · 2023-12-29Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This work delivers a novel technique to detect brain tumor with the help of enhanced watershed modeling integrated with a modified ResNet50 architecture. It also involves stochastic approaches to help in developing enhanced watershed modeling. Cancer diseases, primarily the brain tumor, have been exponentially raised which has alarmed researchers from academia and industry. Nowadays, researchers need to attain a more effective, accurate, and trustworthy brain tumor tissue detection and classification approach. Different from traditional machine learning methods that are just targeting to enhance classification efficiency, this work highlights the process to extract several deep features to diagnose brain tumor effectively. This paper explains the modeling of a novel technique by integrating the modified ResNet50 with the Enhanced Watershed Segmentation (EWS) algorithm for brain tumor classification and deep feature extraction. The proposed model uses the ResNet50 model with a modified layer architecture including five convolutional layers and three fully connected layers. The proposed method can retain the optimal computational efficiency with high-dimensional deep features. This work obtains a comprised feature set by retrieving the diverse deep features from the ResNet50 deep learning model and feeds them as input to the classifier. The good performing capability of the proposed model is achieved by using hybrid features of ResNet50. The brain tumor tissue images were extracted by the suggested hybrid deep feature-based modified ResNet50 model and the EWS-based modified ResNet50 model with a high classification accuracy of 92% and 90%, 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.