Exploring a Novel Neuroplexus Learning Framework (NLF) Approach for MRI Image Analysis

This study presents the role of focal liver lesions in early diagnosis of lesion growth worldwide. To accurately detect focal liver lesions, we used Magnetic resonance image to identify liver lesions. The early detection and characterization of liver lesions present challenges. This study mainly focused on the identification of lesion morphology, size, appearance, and artifacts in medical images. They proposed deep learning using a convolutional neural network (CNN) and several algorithms for medical image processing and image segmentation methods. In our proposed system, we introduced a CNN algorithm enhanced by an Innovative Neuroplexus Learning Framework (NLF). By introducing contrast enhancement, image transformation, and filtering, an image-learning framework for detecting liver lesions from magnetic resonance imaging (MRI) can be developed. Images from MRI scans for training, validation, and testing were taken from the public liver tumor dataset and pre-processed for better results of liver tissue and tumors. In our previous methods, they used computed tomography with automated diagnosis for lesions; however, they lacked complications, overlapping vascular issues, low accuracy, and difficulty in identifying small liver lesions. To overcome this issue, we need to develop a new methodology for obtaining the best results using deep learning algorithms and imaging techniques.