  Electron-tracking Compton camera, which is a complete Compton camera with
tracking Compton scattering electron by a gas micro time projection chamber, is
expected to open up MeV gamma-ray astronomy. The technical challenge for
achieving several degrees of the point spread function is the precise
determination of the electron-recoil direction and the scattering position from
track images. We attempted to reconstruct these parameters using convolutional
neural networks. Two network models were designed to predict the recoil
direction and the scattering position. These models marked 41$~$degrees of the
angular resolution and 2.1$~$mm of the position resolution for 75$~$keV
electron simulation data in Argon-based gas at 2$~$atm pressure. In addition,
the point spread function of ETCC was improved to 15$~$degrees from
22$~$degrees for experimental data of 662$~$keV gamma-ray source. These
performances greatly surpassed that using the traditional analysis.
