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.