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Digital Inline Holography (DIH) based microscopy is a well-established technique for the characterization of nano and microparticles, such as biological cells, artificial microparticles, quantum dots, etc. Due to its simplicity and cost-effectiveness, various practical solutions such as auto characterization of complete blood count (CBC), cell viability test, and 3D cell tomography have been developed. In our previous work, we demonstrated the feasibility of this system to perform complete blood count along with the auto characterization of cell-lines as well as shape and size characterization of the microparticles. However, its performance suffered due to the weak signals from some of the cells owing to their poor signatures and the presence of background noise. The auto characterization technique therein was based on the parameters determined from our empirical findings, which limit the system in terms of its cellline recognition power. In this work, we try to address these issues by leveraging an artificial intelligence-powered auto signal enhancing scheme as well as adaptive cell characterization technique. The performance comparison of our proposed method with the existing analytical model shows an increase in accuracy to >98% along with the signal enhancement of >5 dB for most cell types like Red Blood Cell (RBC) and White Blood Cell (WBC), except the cancer cells (HepG2 and MCF-7) for which the accuracy is about 84%.