Fuzzy guided ensemble inference system for brain tumor classification.
Gespeichert in:
| Titel: | Fuzzy guided ensemble inference system for brain tumor classification. |
|---|---|
| Autoren: | Kumar MA; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India., Manikandan G; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. Electronic address: g.manikandan@vit.ac.in., Richard L; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India., Sanjana P; School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. |
| Quelle: | Brain research [Brain Res] 2025 Dec 15; Vol. 1869, pp. 150030. Date of Electronic Publication: 2025 Nov 01. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: Elsevier/North-Holland Biomedical Press Country of Publication: Netherlands NLM ID: 0045503 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1872-6240 (Electronic) Linking ISSN: 00068993 NLM ISO Abbreviation: Brain Res Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Amsterdam Elsevier/North-Holland Biomedical Press. |
| MeSH-Schlagworte: | Brain Neoplasms*/classification , Brain Neoplasms*/diagnostic imaging , Brain Neoplasms*/diagnosis , Fuzzy Logic* , Neural Networks, Computer*, Humans ; Magnetic Resonance Imaging/methods ; Brain/diagnostic imaging |
| Abstract: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The abnormal growth of cells inside or near the brain is called a brain tumor. Brain tumors can be benign (non-cancerous) or malignant (cancerous). Both these types can exert pressure on the surrounding brain tissue, increasing intracranial pressure. As the tumor grows, it presses on the nerves and brain tissues, causing symptoms like persistent headaches, seizures, vision or hearing issues and changes in personality, coordination and balance, these of which will completely ruin the normal life of people. Since treating these tumors is very difficult at the late stages, it is highly significant to find them at the early stages. Understanding the importance of early identification of brain tumors, a fuzzy logic-based ensemble method using Convolutional Neural Networks (CNN) named Fuzzy Guided Ensemble Inference System (FGEIS) is proposed. It is developed to identify tumors from MRI images with a high success rate. The FGEIS approach uses ensemble learning that encompasses variants of four different architectures - Densenet, Resnet, VGG, and Mobilenet. While Resnet's residual connections allow for effective hierarchical feature learning for a variety of tumor types, Densenet supports feature reuse by collecting fine-grained tumor textures. The model is suitable for clinical usage because VGG prioritizes local spatial details that are important for accurate tumor localization, while mobilenet provides computing efficiency. These high-performing models are then integrated and applied through a fuzzy logic system. The experiments show improved performance of ensemble models over individual models with higher classification accuracy of 99.85 percentage. (Copyright © 2025 Elsevier B.V. All rights reserved.) |
| Contributed Indexing: | Keywords: Brain Tumor; Convolutional Neural Network (CNN); Densenet; Ensemble learning; Fuzzy Logic; MRI scans; Mobilenet; Resnet; VGG |
| Entry Date(s): | Date Created: 20251102 Date Completed: 20251116 Latest Revision: 20251116 |
| Update Code: | 20251117 |
| DOI: | 10.1016/j.brainres.2025.150030 |
| PMID: | 41177221 |
| Datenbank: | MEDLINE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />The abnormal growth of cells inside or near the brain is called a brain tumor. Brain tumors can be benign (non-cancerous) or malignant (cancerous). Both these types can exert pressure on the surrounding brain tissue, increasing intracranial pressure. As the tumor grows, it presses on the nerves and brain tissues, causing symptoms like persistent headaches, seizures, vision or hearing issues and changes in personality, coordination and balance, these of which will completely ruin the normal life of people. Since treating these tumors is very difficult at the late stages, it is highly significant to find them at the early stages. Understanding the importance of early identification of brain tumors, a fuzzy logic-based ensemble method using Convolutional Neural Networks (CNN) named Fuzzy Guided Ensemble Inference System (FGEIS) is proposed. It is developed to identify tumors from MRI images with a high success rate. The FGEIS approach uses ensemble learning that encompasses variants of four different architectures - Densenet, Resnet, VGG, and Mobilenet. While Resnet's residual connections allow for effective hierarchical feature learning for a variety of tumor types, Densenet supports feature reuse by collecting fine-grained tumor textures. The model is suitable for clinical usage because VGG prioritizes local spatial details that are important for accurate tumor localization, while mobilenet provides computing efficiency. These high-performing models are then integrated and applied through a fuzzy logic system. The experiments show improved performance of ensemble models over individual models with higher classification accuracy of 99.85 percentage.<br /> (Copyright © 2025 Elsevier B.V. All rights reserved.) |
|---|---|
| ISSN: | 1872-6240 |
| DOI: | 10.1016/j.brainres.2025.150030 |