Application of Artificial Neural Network to Live Predict Brain Lesions like Multiple Sclerosis, Glioma, Glioblastoma and Metastases and Superiority of Refractive Index Over other Parameters
Keywords:Artificial Neural Network (ANN), Magnetic Resonance Imaging (MRI), Metabolites of MR Spectroscopy, Refractive Index (RI), Independent Numeric and dependent Variable, Prediction
Artificial Neural Network an extremely authoritative method of Supervised Machine Learning was applied to detect the different pathological lesions in the brain, like multiple sclerosis MS, glioma of different grades and metastasis. Structural changes in the brain lesions may be noticed in MR images. MR spectroscopic graph may be informative to some extent but is not so easy to diagnose the disease accurately always. Use of ANN helps identifying the condition in doubtful cases. ANN train different data collected from various patients such as – Refractive Index, T2 relaxation values, Apparent Diffusion Coefficient (ADC), Creatine (CR), Choline (CHO), NAA (N-Acetyl Aspartate), ratio of CR/NAA, LIP/LAC (Lipid/lactate), MI ( Myoinositol), CHO/CR and T2 value in the periphery of lesion. Prediction by ANN after training the data, shows high accuracy in diagnosis. RI was found to be unique and most accurate amongst these parameters.
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