Update README.md

README.md

@@ -9,9 +9,6 @@ pipeline_tag: image-segmentation
 
 [***CADET***](https://tomasplsek.github.io/CADET/) is a machine learning pipeline trained for identification of surface brightness depressions (so-called *X-ray cavities*) on noisy *Chandra* images of early-type galaxies and galaxy clusters. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The pipeline is further described in [Plšek et al. 2023](https://arxiv.org/abs/2304.05457).
 
-<!-- The pipeline was developed in order to improve the automation and accuracy of X-ray cavity detection and size-estimation.  -->
-The architecture of the convolutional network consists of 5 convolutional blocks, each resembling an Inception layer, it was implemented using *Keras* library and it's development was inspired by [Fort et al. 2017](https://ui.adsabs.harvard.edu/abs/2017arXiv171200523F/abstract) and [Secká 2019](https://is.muni.cz/th/rnxoz/?lang=en;fakulta=1411). For the clustering, we utilized is the *Scikit-learn* implementation of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN, [Ester et al. 1996](https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.121.9220)).
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 ![Architecture](figures/architecture.png)
 
 ## How to use