Abstract
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.
Highlights
Artificial intelligence has been a hot topic in research and is an omnipresent concept when it comes to medical imaging
In the field of medical image analysis, artificial intelligence is very useful in two areas: image classification and organ segmentation
Image segmentation algorithms [2] images are commonly used on all modalities of imaging like x-rays and magnetic resonance imaging scans, ultrasound, and computed tomography was performed with positron emission tomography
Summary
Artificial intelligence has been a hot topic in research and is an omnipresent concept when it comes to medical imaging. This term, sometimes a bit overused, gives rise to false ideas or feeds hopes of a medical revolution. From the multiple information contained in software and machines using artificial intelligence, medical professionals have powerful and efficient tools to improve their diagnosis. In the field of medical image analysis, artificial intelligence is very useful in two areas: image classification and organ segmentation. Image classification algorithms can aid in the diagnosis by classifying the image into a specific category of pathology. Chest x-ray is often more common with other medical imaging [3]
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