An Efficient Multiple Sclerosis Segmentation Framework using Hybrid Dilated Convolution –based Adaptive Mobilenet Mechanism.

Abstract

Multiple Sclerosis (MS) is considered a very popular neurological condition in adults. Poor walking stability is considered the primary sign of MS. The Magnetic Resonance Imaging (MRI) is ensured to be a delicate approach for discovering disease progression. Severity quantification of the disease via MS lesion volume validation with MR imaging is significant for monitoring and learning the disorder and its treatment. Numerous techniques for MS lesions segmentation demand experts seed points as input, yet do not sufficiently permit the specialists to perform effectively or intuitively. Interestingly, various approaches also consider that the points are optimally determined. Hence, it is necessary to rectify some of the troubles that existed in classical MS classification mechanisms by developing a powerful process with deep learning in this work. In the beginning, the requisite MS images are fetched from the existing online source and forwarded to the module of image segmentation. In this module, an efficient image segmentation operation is carried out employing Transformer Unet++ with MobileNetv3 (TUnet++-MNetv3). Further, the segmented images are given to the module named MS classification, where the Hybrid Dilated Convolution based Adaptive MobileNet (HDC-AMNet) is recommended to classify the MS. Moreover, the Enhanced Single Candidate Optimizer (ESCO) is adopted to optimize the parameters that exist in the MobileNet. Thus, the designed MS classification task achieves improved performance rates by contrasting it with its baseline techniques employing numerous metrics.