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@article{196275,
        author = {S.V.Rajiga and Dr.M.Gunasekaran},
        title = {ANALYTICAL STUDY OF HYBRID MODES OF TECHNIQUES USED TO DETECT HYDROCEPHALUS FROM MRI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {2855-2859},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196275},
        abstract = {Hydrocephalus is a condition in which fluid accumulates in the brain, typically in young children, enlarging the head and sometimes causing brain damage. The most common cause of hydrocephalus is obstruction of cerebrospinal fluid (CSF) flow, often due to congenital defects like aqueductal stenosis. In new borns, it is frequently caused by intraventricular haemorrhage (bleeding) related to prematurity. Other major causes include meningitis, tumors, and head injuries. If it is found earlier there is a possibility to cure the disease as well as reduce the risk of disease. In this paper, discussed about three hybrid modes of techniques from Data Mining, Image Processing and Deep Learning. Data Mining concepts used to mine the exact data from the taken MRI image. Image processing techniques used to process the mined data as per the requirement. Deep Learning algorithms finds the exact lesion of the disease and its cause. By applying all these techniques in hybrid mode, it is easy to find the cause of disease and cure the disease in child hood stage itself. From Data Mining Preprocessing and Segmentation techniques used to mine the data. Using Image Processing concept images are scaled upto the necessity. From Deep Learning Conventional Neural Network algorithms are used to categorise the images and predict the risk of the disease.},
        keywords = {Hydrocephalus, CSF, Data Mining, Image Processing, Deep Learning, CNN.},
        month = {April},
        }