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@article{202960,
        author = {Prof. Kokare S. A. and Sabaa Shafique Mujawar and Shruti Mohan Baravkar and Priti Vijay Jadhav and Dr. Shah Saloni Niranjan},
        title = {Chest X-ray Outliers Detection Model Using Dimension Reduction and Edge Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {8419-8427},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202960},
        abstract = {The increasing demand for computer-aided diagnosis in healthcare has created opportunities for Artificial Intelligence (AI) and Machine Learning (ML) techniques in medical image analysis. Chest X-rays are among the most commonly used diagnostic tools for detecting pulmonary diseases such as pneumonia, tuberculosis, lung infections, and COVID-19-related abnormalities. However, manual interpretation of radiographic images requires experienced radiologists and may lead to delays, inconsistencies, and diagnostic errors when handling large volumes of medical data.
This paper presents a Chest X-Ray Outlier Detection Model using Dimension Reduction and Edge Detection techniques for identifying abnormal patterns in chest radiographs. The proposed system combines image preprocessing, feature extraction, edge enhancement, dimensionality reduction, and machine learning based classification to improve diagnostic efficiency. Edge detection methods are used to highlight important anatomical structures and lung boundaries, while Principal Component Analysis (PCA) is employed to reduce redundant features and computational complexity.
The proposed framework improves image representation, enhances abnormality detection, and assists healthcare professionals in preliminary disease screening. Experimental observations demonstrate that the integration of edge detection and dimensionality reduction improves classification efficiency while maintaining meaningful diagnostic features. The system can contribute toward intelligent healthcare systems, especially in resource-constrained medical environments.},
        keywords = {Machine Learning, Chest X-ray, Medical Imaging, PCA, Edge Detection, Outlier Detection, Artificial Intelligence, Computer Vision.},
        month = {May},
        }