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We present a classification of the light curve morphologies of eclipsingbinary systems observed by ASAS-SN based on their light curve images. The dataof 16500 eclipsing systems having three different classes (detached Algol type,$\beta$ Lyr type, and W UMa type) are collected to construct their lightcurves. A deep learning algorithm containing the convolutional neural networksis employed on the images to achieve a satisfying classification. A code calledASEBCLASS is written in Python language, and it uses the TensorFlow platformthrough Keras API to employ the mathematical libraries in the training phase ofthe model. The architecture consists of four groups of convolutional,activation, maximum pooling layers together with the additional fully connectedlayers. The results show that our algorithm estimates the morphological classof an external input image data with an accuracy value of 92%.

Image Classification Algorithm for Determining the Light Curve Morphologies of ASAS-SN Eclipsing Binaries