Open Access Open Access  Restricted Access Subscription Access

Neuro Deep Fuzzy Genetic Algorithm Approach for Classification and Detection of Brain Tumor from Large Datasets


Affiliations
1 Department of Electronics and Communication Engineering, St Xavier’s Catholic College of Engineering, India
2 Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakannur
3 Department of Electronics and Communication Engineering, Vishnu Institute of Technology, India
4 Department of Data Science, Saint Peter's University, United States

   Subscribe/Renew Journal


Brain 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.

Keywords

Brain Tumor Classification, Deep Learning, Fuzzy Logic, Genetic Algorithms, MRI Dataset
Subscription Login to verify subscription
User
Notifications
Font Size

Abstract Views: 37




  • Neuro Deep Fuzzy Genetic Algorithm Approach for Classification and Detection of Brain Tumor from Large Datasets

Abstract Views: 37  | 

Authors

Mary Vasanthi Soosaimariyan
Department of Electronics and Communication Engineering, St Xavier’s Catholic College of Engineering, India
T. Jayasree
Department of Electronics and Communication Engineering, Government College of Engineering, Bodinayakannur
P. Sri Lekha
Department of Electronics and Communication Engineering, Vishnu Institute of Technology, India
Vijay Kumar Reddy Voddi
Department of Data Science, Saint Peter's University, United States

Abstract


Brain 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.

Keywords


Brain Tumor Classification, Deep Learning, Fuzzy Logic, Genetic Algorithms, MRI Dataset