Miniaturized fluorescence microscopy has revolutionized the way neuroscientists study the brain in-vivo. Recent developments in computational lensless imaging promise a next generation of miniaturized microscopes in lensless fluorescence microscopy. We developed a microscope prototype using an optimized Fresnel amplitude mask. While many lensless imaging modalities have reported excellent performance using Deep Learning (DL) approaches, DL application in fluorescence imaging has been left untouched. We generated a computational dataset based on experimental system calibration to evaluate DL capabilities on biological cell morphologies. We show that our DL-assisted microscope can provide high-quality imaging with a structural similarity index of 89%. The least absolute error was decreased by 63% using the DL-assisted method compared with the classical models. The state-of-the-art performance of this prototype enhances the expected potential of amplitude masks in lensless microscopy applications, which are critical for robust in-vivo flat microscopy with engineered image sensors.Clinical Relevance- This study aids in advancing miniaturized fluorescence microscopy, which greatly impacts long-term brain circuit and disease studies in freely moving animal models.