ArticleDiagnostics (Basel, Switzerland)2023
Recognizing Brain Tumors Using Adaptive Noise Filtering and Statistical Features.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Improved brain tumor classification through DenseNet121 based transfer learning.Discover oncology · 2025Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- An effective ensemble learning approach for classification of glioma grades based on novel MRI features.Scientific reports · 2024Article
- Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.Journal of cancer research and clinical oncology · 2024Article
- A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics.Journal of cancer research and clinical oncology · 2023Review
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Authors and funding
7 authors.
Funding
Abstract
The human brain, primarily composed of white blood cells, is centered on the neurological system. Incorrectly positioned cells in the immune system, blood vessels, endocrine, glial, axon, and other cancer-causing tissues, can assemble to create a brain tumor. It is currently impossible to find cancer physically and make a diagnosis. The tumor can be found and recognized using the MRI-programmed division method. It takes a powerful segmentation technique to produce accurate output. This study examines a brain MRI scan and uses a technique to obtain a more precise image of the tumor-affected area. The critical aspects of the proposed method are the utilization of noisy MRI brain images, anisotropic noise removal filtering, segmentation with an SVM classifier, and isolation of the adjacent region from the normal morphological processes. Accurate brain MRI imaging is the primary goal of this strategy. The divided section of the cancer is placed on the actual image of a particular culture, but that is by no means the last step. The tumor is located by categorizing the pixel brightness in the filtered image. According to test findings, the SVM could partition data with 98% accuracy.
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Registered trials
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