Integrated Deep Learning Approaches for Robust Medical Image Reconstruction and Abnormality Classification
Year: 2025
Type: article
Abstract: This project proposal outlines the development of an automated image processing system designed to assist in the early and accurate detection of diseases from medical imaging modalities including MRI, CT, Ultrasound, and X-ray. The proposed system will leverage advanced image processing techniques for artifact reduction, noise suppression, and resolution enhancement, coupled with machine learning methodologies for feature extraction and abnormality classification. The primary objective is to enhance diagnostic accuracy and efficiency, thereby supporting clinical decision-making. This document details the background, problem statement, specific objectives, a review of pertinent literature, the proposed methodology including algorithmic considerations, expected outcomes, and a plan for evaluation. The ultimate aim is to design a system that improves image quality for better visual interpretation and provides a robust framework for automated disease detection, while also considering computational efficiency for practical deployment.
Authors: Arslan Hassan, Adeel Abbas
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949474844
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