ArticlePeerJ. Computer science2021
Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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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.
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Who cites it
7 citing papers in PubMed.
- MISM: A Medical Image Segmentation Metric for Evaluation of Weak Labeled Data.Diagnostics (Basel, Switzerland) · 2023Article
- Variational Autoencoders-BasedSelf-Learning Model for Tumor Identification and Impact Analysis from 2-D MRI Images.Journal of healthcare engineering · 2023Article
- Using Recurrent Neural Networks for Predicting Type-2 Diabetes from Genomic and Tabular Data.Diagnostics (Basel, Switzerland) · 2022Article
- AAL and Internet of Medical Things for Monitoring Type-2 Diabetic Patients.Diagnostics (Basel, Switzerland) · 2022Article
- Empirical Study on Classifiers for Earlier Prediction of COVID-19 Infection Cure and Death Rate in the Indian States.Healthcare (Basel, Switzerland) · 2022Article
- A Precise Medical Imaging Approach for Brain MRI Image Classification.Computational intelligence and neuroscience · 2022Article
- Software Fault Localization through Aggregation-Based Neural Ranking for Static and Dynamic Features Selection.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach's performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.
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Registered trials
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