MPNBT: Design of an Efficient Multimodal Prediction Model for Neurological Disorders using Bioinspired Transfer-Learning
Keywords:
Brain, Binary, Cord, Convolutions, Entropy, Frequency, Flame, Gabor, Moth, Spinal, Tumour, Wavelet.Abstract
Magnetic resonance imaging (MRI) classify cancers and other diseases. This is a multi-domain problem that calls for the segmentation of the affected regions, their representation as feature vectors, the development of region-specific classifiers, and post-processing methods. The segmentation performance of current illness identification algorithms is either subpar or their complexity is increased when compared to differential-position MRI and functional magnetic resonance imaging (fMRI) datasets & samples. The additional challenge is that these algorithms can accurately classify fewer ailments. This article suggests the use of bio-inspired transfer-learning methods to create a multimodal neurological disease prediction model. To accurately identify tumor-specific regions, the recommended approach first segments MRI and fMRI images with MRA CNN (Masked-Region Augmented Convolutional Neural Network). Gabor analysis, wavelet analysis, Frequency analysis, convolution analysis, and entropy analysis are applied to transform these regions into multidomain features. The feature sets are collected, and a Moth Flame Optimizer (MFO) analyses them to determine which feature sets have the most variance. These feature sets are then categorized as "tumor" or "non-tumor" with the Binary Convolutional Neural Network (BCNN) method. With this method, feature vectors are split into two groups and given a common example for each group. The BCNN employs most of the functions to detect tumors compared to conventional cancer detection algorithms, which improves classification accuracy by 4.9%, precision by 2.8%, recall by 3.5%, and time by 4.1%. Using MRIs of the brain and spinal cord to test the model, it may be successfully modified to fit different circumstances.
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