Hybrid Approaches to MRI Image Enhancement: Integrating Deep Learning and Traditional Image Processing Methods

Abstract

MRI is a critical device in clinical diagnostics, offering essence experiences into delicate tissue structures. In any case, the nature of MRI can be undermined by different elements, including noise, low contrast, and antiquities, which might upset exact determination. This paper presents an integrated methodology that combines conventional image processing procedures with deep learning (DL) models to upgrade MRI image quality. Conventional techniques, like Gaussian filtering, histogram equalization, and edge detection, are first applied to preprocess the image, diminishing noise and further developing contrast. In this way, advance DL designs, especially CNNs, are utilized to additionally refine and upgrade the image by learning complex examples and elements. The proposed hybrid method performs better in terms of image clarity, contrast enhancement, and noise reduction. Broad investigations on benchmark MRI datasets show that this combination fundamentally works on indicative precision and dependability, making it a promising device for clinical applications. CNN and VGG 19 are used for interpreting and analyzing visual imagery. In this paper, Hybrid model are used to detect MRI Images and analysis can be done on basis of system performance i.e. accuracy and loss.