Paper Title :Predicting Lung Diseases from X-Rays: A Dense Convolutional Neural Network Approach
Author :Sudhanshu, Krishan Kumar
Article Citation :Sudhanshu ,Krishan Kumar ,
(2024 ) " Predicting Lung Diseases from X-Rays: A Dense Convolutional Neural Network Approach " ,
International Journal of Advance Computational Engineering and Networking (IJACEN) ,
pp. 51-55,
Volume-12,Issue-6
Abstract : Early and accurate diagnosis of lung diseases from chest X-rays plays a crucial role in improving patient
outcomes and reducing healthcare costs. In this study, we propose a Dense Convolutional Neural Network (CNN) model for
the automated prediction of lung diseases based on X-ray images. The proposed model integrates dense connections within
convolutional layers, allowing for efficient feature reuse and gradient flow throughout the network. We utilize a
comprehensive dataset comprising X-ray images representing various lung diseases, including pneumonia, tuberculosis, and
lung cancer, as well as normal cases. The dataset is preprocessed to enhance the quality of input images and normalize
intensity levels. Through extensive experimentation and validation, we demonstrate the efficacy of our Dense CNN model in
accurately classifying lung diseases. We evaluate the model's performance using metrics such as accuracy, precision, recall,
and F1-score, achieving promising results compared to existing approaches.
Keywords - CNN, Lungs Disease, X-Ray
Type : Research paper
Published : Volume-12,Issue-6
Copyright: © Institute of Research and Journals
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Published on 2024-09-21 |
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