报告详情
MRI-Based Brain Tumor Classification Using Deep Feature Extraction and Metaheuristic Feature Selection for Predictive Modeling
编号：137
访问权限：仅限参会人
更新：2025-12-23 13:12:34 浏览：124次
拓展类型2
报告开始：2025年12月30日 16:30（Asia/Amman）
报告时间：15min
所在会场：[S7] Track 7: Pattern Recognition, Computer Vision and Image Processing [S7-2] Track 7: Pattern Recognition, Computer Vision and Image Processing
摘要
Intracranial tumors are life-threatening and severe health hazards attributed to the unregulated and excessive growth of brain cells; thus, a diagnosis needs to be made at an early and accurate stage to achieve successful treatment. As much as medical practitioners tend to involve Brain MRI in diagnostic purposes, some cases of oversight or misdiagnosis may be caused by human interpretation. Machine learning-based detection can be effectively used to reduce this issue. In medical imaging experiments, especially in essential medical fields, such as brain tumor classification, the method of feature selection is also vital in enhancing the effectiveness and performance of models. The suggested study applies a pre-trained transfer learning model (MobileNet) to extract deep features, namely, textures, edges, lines, and shapes of objects. However, such high-dimensional features are effective; they can involve redundant and irrelevant factors that negatively impact on the model performance. The best tool in establishing the most pertinent subset of features is a powerful metaheuristic optimization plan. This is useful to reduce dimensionality efficiently as well as preserve the ability to discriminate. The proposed research offers a new hybrid system that brings to bear the TL with a nature-inspired Particle Swarm Optimization (PSO) algorithm used to select the best features based on the Brain MRI data. The proposed PSO-based feature selection algorithm proves good results when used in a Support Vector Classifier with a score of the R2 as 93.7211%, a precision reached 95.2828%, an accuracy of 95.0995%, a recall as 95.0995%, and an F1-score as 95.0996%, which shows the effectiveness of the proposed framework for classifying the brain tumor.
关键词
Brain Tumor, Machine Learning, Transfer Learning, Particle Swarm Optimization, Support Vector Machine, Medical Image Processing, and Healthcare.
报告人
BISWAJIT TRIPATHY
Ph.D. Scholar Birla Institute of Technology稿件作者
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重要日期
-
会议日期
12月29日
2025
至12月31日
2025
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12月30日 2025
报告提交截止日期
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02月10日 2026
初稿截稿日期
-
02月10日 2026
注册截止日期
主办单位
国际科学联合会
承办单位
扎尔卡大学
联系方式
- Miss AsianComNet
- as******@usssociety.org
历届会议
-
2024年10月24日 泰国 Bangkok
2024亚洲通讯与网络会议
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