The development of label-free, high-speed, automated and integrated cell sorting solutions is of particular interest for several biomedical applications. The employment of digital holographic microscopy in microfluidic flow cytometry gives access to a large amount of information regarding the 3D refractive index structure of a cell. In the presented work a passive, linear, integrated photonic stage is proposed as an effective nonlinear mixing interface between the hologram projection and the image sensor, allowing for a fast, compact and power-efficient extreme learning machine (ELM) implementation. The required nonlinearity comes from the sinusoid-based transfer function between the phase-shift accumulated by the light through the cell and the field intensity measured by the detector. 2D FDTD simulations with 2 classes of randomized cell models (normal and cancer cells differing in their average nucleus size) have been employed to train and test a readout linear classifier. A collection of silicon nitride pillar scatterers embedded in a silica cladding are interposed between the cell and the intensity monitor, in order to increase the complexity of the acquired interference pattern and to assist the readout linear classifier. The results show that, employing green light, the presence of scatterer layers decreases the classification error rate up to ~ 50% with respect to the case without scatterers. Such improvement can be further increased to a factor ~ 5 when a properly designed integrated optical cavity containing the cell is considered. An intuitive argumentation that explains these results is provided.