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Impact of image enhancement methods on lung disease diagnosis using x-ray images

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Abstract

Nowadays, Lung disease is the most common and fatal disease around the world. It causes patients to experience shortening of breath, fever, and fatigue. Generally, lung disease can be detected with X-ray and CT scan. However, in conditions where the difference between the normal and infected image is very small, image enhancement should be employed to improve the quality of the image. This proposed work aims to demonstrate the better image enhancement method for the detection of lung diseases. The different approaches, such as histogram equalization (HE), Min–Max contrast stretching (MMCS), and contrast constrained adaptive histogram equalization (CLAHE) at the pre-processing level to improve the contrast of the processed image. For the classification of different chest X-ray images, four pre-trained deep neural networks (Inceptionresnetv2, Xception, DenseNet121 and InceptionV3) have been used. Among the evaluated classification models, inceptionv3 with CLAHE achieves the highest accuracy of 96.66% for multi-classification. The designed approach further presents the potential to boost fast and vigorous lung disease detection by using X-ray images with very reliable and comparable performance than previous studies that included one or two model and techniques for image enhancement.

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Authors and Affiliations

  1. Electronics and Communication, Thapar University, Patiala, Punjab, India

    Prashant Bhardwaj & Amanpreet Kaur

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  1. Prashant Bhardwaj
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  2. Amanpreet Kaur
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Correspondence to Prashant Bhardwaj.

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Bhardwaj, P., Kaur, A. Impact of image enhancement methods on lung disease diagnosis using x-ray images. Int. j. inf. tecnol. 15, 3521–3526 (2023). https://doi.org/10.1007/s41870-023-01409-1

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