000			a
999			_c22714 _d22714
003			OSt
005			20250424150625.0
008			250424b xxu||||| |||| 00| 0 eng d
040			_aAIKTC-KRRC _cAIKTC-KRRC
100			_925985 _aSoosaimariyan, Mary Vasanthi
245			_aNeuro deep fuzzy genetic algorithm approach for classification and detection of brain tumor from large datasets
250			_aVol.15(2), Oct
260			_aChennai _bICT Academy _c2024
300			_a3473-3481p.
520			_aBrain tumors represent one of the most critical and life-threatening diseases. Early detection and accurate classification are essential for effective treatment planning and survival rate improvement. With the exponential growth of medical data, particularly in imaging and diagnostic datasets, traditional algorithms face limitations in handling large-scale, high-dimensional data efficiently. Conventional machine learning and diagnostic methods often struggle with classification accuracy, computational complexity, and overfitting in large, noisy datasets. Addressing these issues is crucial for the development of robust diagnostic tools capable of handling real-world clinical data. This paper presents a novel hybrid approach combining Neural Networks, Deep Learning, Fuzzy Logic, and Genetic Algorithms, termed Neuro Deep Fuzzy Genetic Algorithm (NDFGA), for the classification and detection of brain tumors from large datasets. Neural Networks and Deep Learning architectures are leveraged for feature extraction and hierarchical learning. Fuzzy Logic improves interpretability and manages uncertainty in medical data, while Genetic Algorithms optimize feature selection and model parameters. This hybrid method is designed to maximize classification accuracy while minimizing false positives and computational overhead. The proposed approach was tested on a large brain tumor dataset comprising over 10,000 MRI scans. The NDFGA approach demonstrated superior performance compared to standalone methods. It achieved a classification accuracy of 97.8%, a sensitivity of 96.5%, and a specificity of 98.1%. The model also showed improved robustness in handling large datasets, reducing false positives by 12% and computational time by 15% compared to traditional methods. This hybrid model presents a scalable and efficient solution for brain tumor detection, especially in clinical environments.
650		0	_94622 _aComputer Engineering
700			_925986 _a Jayasree, T.
773	0		_tICTACT Journal on Soft Computing (IJSC) _dChennai ICT Academy
856			_uhttps://ictactjournals.in/paper/IJSC_Vol_15_Iss_2_Paper_1_3473_3481.pdf _yClick here
942			_2ddc _cAR