Systemic lupus erythematosus is a disease in which the immune system attacks host tissues. One organ commonly attacked is the kidney, in which case the resultant acute and chronic damages are called lupus nephritis. The accumulated damage can result in renal failure. The percutaneous renal biopsy is invaluable to the assessment of the disease and its therapeutic response. A large portion of the pathological assessment is done by histological analysis of the biopsied tissue with light microscopy. Computational models can alleviate a portion of expert disagreement by providing unified, reproducible quantifications of digitized image structures. In this work, we perform fully automated whole slide segmentation of glomeruli from Periodic Acid-Schiff (PAS), hematoxylin and eosin, silver, and trichrome stained lupus nephritis biopsies. The automatically extracted PAS glomeruli are quantified by a set of 285 hand-crafted features designed specifically to target glomerular lesions in lupus nephritis. These features are fed in sequence to a recurrent neural network architecture which views multiple glomerular features from a single biopsy, and outputs a continuous diagnostic value representative of classes II-V of the scheme by Weening et al. On 82 whole slide images taken from 65 patients, compared to renal pathologist annotations and using only the PAS stain, the network achieved a Cohen's kappa of 0.42 with 95% confidence interval [0.32, 0.52] to render the correct class chosen from II-V, and 0.56, 95% CI [0.43, 0.69] to render an additional class V diagnosis when required.