Abstract
Unlike the identification of subjects with salient features in natural images, the visual similarity between the pathological features of Chest X-Ray (CXR) images complicates the distinction and interpretation of pneumonia signs. In this work, we aim to enhance Chest X-ray images by applying several image pre-processing techniques such as histogram equalization, contrast limited adaptive histogram equalization (CLAHE) and Unsharp mask. For instance, the fact of being the most performant natural image enhancement algorithms raises the question of whether they can achieve similar performance on CXR imagery. Hence, our objective is to investigate these enhancement techniques for the task of Pneumonia classification with deep neural architectures. To validate our findings, we provide a comparative study on the largest public pneumonia dataset of the Radiological Society of North America (RSNA) dubbed “Pneumonia Detection Challenge Dataset”. The performance was measured in the two cases of balanced and imbalanced positive (pneumonia) and negative (no pneumonia) classes, where the preprocessing is intended to mitigate the bias toward the dominant class.
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Nefoussi, S., Amamra, A., Amarouche, I.A. (2021). A Comparative Study of Chest X-Ray Image Enhancement Techniques for Pneumonia Recognition. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds) Advances in Computing Systems and Applications. CSA 2020. Lecture Notes in Networks and Systems, vol 199. Springer, Cham. https://doi.org/10.1007/978-3-030-69418-0_25
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