Advancing Healthcare With Deep Learning: Innovations In Medical Image Analysis

Abstract

Image detection and classification is one of the most crucial and emergent areas in the image recognition and analysis. Detection and classification can be used for a variety of computer vision and digital image processing applicationsDeep Learning (DL) has demonstrated outstanding performance in tasks such as detection and classification, making it highly effective for medical image analysis in healthcare systems. By integrating DL techniques, researchers aim to address key challenges in the medical field—particularly in the analysis of medical images for accurate detection and classification.

In Medical image analysis, the detection and classification of MRI images are required for precise and accurate, as well as computationally efficient, image processing algorithms. The success of the image detection and classification depends on the reliability and accuracy of the processing of the MRI images. In this paper we proposed pre-processing, post processing for different MR based images. The pre-processing is image containing image acquisition, filtering the images, then we followed post-processing containing grey scale-based segmentation and classification using CNN.

In this paper, we propose the use of a dataset comprising T1-weighted, T2-weighted, and FLAIR MRI images for brain image analysis. The intensity values in these images are primarily influenced by the relaxation times associated with T1 and T2 sequences. The contrast levels vary between T1- and T2-weighted images, providing complementary information for accurate analysis.

We are proposed in this paper MR image detection and classification Using deep learning method that is the CNN. The proposed algorithm is calculated using accuracy, index of similarity (SI), Sensitivity and precision and also specificity are also calculated for better estimation to the proposed method.