International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Research Paper | Computer Science and Information Technology | India | Volume 13 Issue 1, January 2024

A CONV-RFDNN Model for the Classification and Detection of Lung Diseases on Chest X-Rays Using Transfer Learning

Vidyasri S. | Dr. Saravanan S.

Abstract: Identifying and categorizing lung diseases involves the application of advanced techniques such as Computer Vision (CV) and Machine Learning (ML). These methodologies play a crucial role in recognizing and classifying ailments affecting lung health. By employing Computer Vision and Machine Learning, medical professionals can swiftly identify and manage lung diseases, contributing to enhanced healthcare outcomes. This utilization of technology mirrors its role in healthcare, where these methods assist medical professionals in identifying and managing lung diseases to ensure effective diagnoses and treatment strategies. Deep Learning (DL), a subset of Artificial Intelligence (AI), has proven successful in automating the detection and classification of lung diseases. This research leverages the CONV-RFDNN model for the Automatic Detection and Classification of Lung Diseases. The process begins with a pre-processing stage, involving tasks such as image resizing and the application of a Bilateral filter to enhance image quality. Feature extraction is then performed using a neural network architecture like VGG-19. Finally, the extracted features are input into a classification model, such as Random Forest (RF), to differentiate between various lung disease types. A thorough analysis of experimental results reveals that the CONV-RFDNN model outperforms recent approaches, demonstrating superior performance in lung disease detection and classification.

Keywords: Computer Vision, Machine Learning, Deep Learning, VGG-19, Random Forest, Transfer Learning

Edition: Volume 13 Issue 1, January 2024,

Pages: 1502 - 1507

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