Tauopathies, a subset of neurodegenerative diseases, are anticipated to surge in incidence in the coming decades. While Machine and Deep Learning algorithms have significantly advanced medical imaging, the potential of post-mortem immunofluorescence imaging of brain tissues, particularly for monitoring Tau protein anomalies, remains largely untapped. This study presents a Convolutional Neural Network pipeline leveraging the ResNet-IFT architecture and Transfer Learning to classify Tau pathologies in Alzheimer's disease and Progressive Supranuclear Palsy using post-mortem immunofluorescence images. Among four tested architectures, our models consistently showcased an average accuracy of 98.41%, shedding light on the unique structural patterns of Tau protein in NFTs.