X-ray diffraction (XRD) is a well-known technique used by scientists and engineers to determine the atomic-scale structures as a basis for understanding the composition-structure-property relationship of materials. The current approach for the analysis of XRD data is a multi-stage process requiring several intensive computations such as integration along 2θ for conversion to 1D patterns (intensity-2θ), background removal by polynomial fitting, and indexing against a large database of reference peaks. It impacts the decisions about the subsequent experiments of the materials under investigation and delays the overall process. In this paper, we focus on eliminating such multi-stage XRD analysis by directly learning the phase regions from the raw (2D) XRD image. We introduce a peak area detection network (PADNet) that directly learns to predict the phase regions using the raw XRD patterns without any need for explicit preprocessing and background removal. PADNet contains specially designed large symmetrical convolutional filters at the first layer to capture the peaks and automatically remove the background by computing the difference in intensity counts across different symmetries. We evaluate PADNet using two sets of XRD patterns collected from SLAC and Bruker D-8 for the Sn-Ti-Zn-O composition space; each set contains 177 experimental XRD patterns with their phase regions. We find that PADNet can successfully classify the XRD patterns independent of the presence of background noise and perform better than the current approach of extrapolating phase region labels based on 1D XRD patterns.