Leveraging transfer learning-driven convolutional neural network-based semantic segmentation model for medical image analysis using MRI images.
Journal:
Scientific reports
Published Date:
Dec 18, 2024
Abstract
Recognition and segmentation of brain tumours (BT) using MR images are valuable and tedious processes in the healthcare industry. Earlier diagnosis and localization of BT provide timely options to select effective treatment plans for the doctors and can save lives. BT segmentation from Magnetic Resonance Images (MRI) is considered a big challenge owing to the difficulty of BT tissues, and segmenting them from the healthier tissue is challenging when manual segmentation is done through radiologists. Among the recent proposals for the brain segmentation method, the BT segmentation method based on machine learning (ML) and image processing could be better. Thus, the DL-based brain segmentation method is extensively applied, and the convolutional network has better brain segmentation effects. The deep convolutional network model has the problem of a large loss of information and a large number of parameters in the encoding and decoding processes. With this motivation, this article presents a new Deep Transfer Learning with Semantic Segmentation based Medical Image Analysis (DTLSS-MIA) technique on MRI images. The DTLSS-MIA technique aims to segment the affected BT area in the MRI images. At first, the presented method utilizes a Median filtering (MF) approach to optimize the quality of MRI images and remove the noise. For the semantic segmentation method, the DTLSS-MIA method follows DeepLabv3 + with a backbone of the EfficientNet model for determining the affected brain region. Moreover, the CapsNet architecture is employed for the feature extraction process. Lastly, the crayfish optimization (CFO) technique with diffusion variational autoencoder (D-VAE) architecture is used as a classification mechanism, and the CFO technique effectively tunes the D-VAE hyperparameter. The simulation analysis of the DTLSS-MIA technique is validated on a benchmark dataset. The performance validation of the DTLSS-MIA technique exhibited a superior accuracy value of 99.53% over other methods.