ITM Web Conf.
Volume 43, 2022The International Conference on Artificial Intelligence and Engineering 2022 (ICAIE’2022)
|Number of page(s)||6|
|Published online||14 March 2022|
System segmentation of Lungs in images chest x-ray using the generative adversarial network
1 TIAD Laboratory, Sciences and Technology Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
2 Igdr Umr 6290 CnrsRennes1 University, Rennes, France
* Corresponding author. E-mail: firstname.lastname@example.org
One of the most common medical imaging methods is a chest x-ray, as it contributes to the early detection of lung cancer compared to other methods. this work presents the use of a generative adversarial network to perform lung chest x-ray image segmentation. The network is two frameworks neural (generator and discriminator). In our work the generator is trained to generate a mask for the input of a given original image, the discriminator distinguishes between the original mask and the generated mask, the final objective is to generate masks for the input. The model is trained and evaluated, well generalized experimental results of the JSRT dataset reveal that the proposed model can a dice score of 0.9778, which is better than other reported state-of-the-art results.
Key words: Deep learning / TensorFlow / JSRT / Segmentation / Medical imaging / Generative Adversarial Networks
© The Authors, published by EDP Sciences, 2022
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