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@article{164073,
        author = {Mr.D.Balaji and Ms.S.Charulatha and Ms.B.Sushmitha and Ms.M.Vineka},
        title = {Multimodal Brain Tumor Detection and Classification in MRI Images Using Convolutional Neural Network},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {208-221},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164073},
        abstract = {A groundbreaking advancement in clinical practice is the automated classification and recognition of brain tumors. This study presents an advanced clinical support system designed to automatically discern the type and progression of brain tumors using finely tuned neural networks. Initial data processing involves Ensemble Genetic filtering and an enhanced graph cut method, succeeded by segmentation through Fuzzy clustering using Local Approximation of Memberships (Segmentation clustering). Feature extraction is then conducted using Gray Level Co-occurrence Matrix (GLCM) and Histogram of oriented gradients (HOG). The model further employs Firefly-optimized Probabilistic Neural Networks for classification and Cuckoo Search-optimized Adaptive Neuro-Fuzzy Inference System (NFS) for tumor identification, resulting in highly precise tumor size and stage determination. The system's efficacy was assessed, achieving an impressive 98.3% accuracy rate. Physicians can leverage the outcomes of this system for informed clinical interventions.},
        keywords = {Automated classification, brain tumors, neural networks, Ensemble Genetic filtering, Segmentation clustering, feature extraction, clinical interventions.},
        month = {},
        }