deep transfer learningbased hologram classification for molecular diagnostics lensfree digital inline holography ldih is a promising microscopic tool that overcomes several drawbacks eg limited field of view of traditional lensbased microcopy however extensive computation is required to reconstruct object images from the complex diffraction patterns produced by ldih this limits ldih utility for pointofcare applications particularly in resource limited settings we describe a deep transfer learning dtl based approach to process ldih images in the context of cellular analyses specifically we captured holograms of cells labeled with molecularspecific microbeads and trained neural networks to classify these holograms without reconstruction using raw holograms as input the trained networks were able to classify individual cells according to the number of cellbound microbeads the dtlbased approach including a vgg19 pretrained network showed robust performance with experimental data combined with the developed dtl approach ldih could be realized as a lowcost portable tool for pointofcare diagnostics