Frontotemporal dementia (FTD) is a devastating neurodegenerative disorder that primarily affects the frontal and temporal lobes of the brain, leading to cognitive decline and behavioral changes. Early and accurate diagnosis of FTD is crucial for initiating timely interventions and providing appropriate care to patients. In the opinion of the experts, about 12-22 persons out of the population of 100,000 persons experience FTD. That means between 1.2 million and 1.8 million people have it worldwide. This research paper proposes a novel deep learning framework that utilizes sparse diffusion measures extracted from neuroimaging data to aid in the early diagnosis of Frontotemporal Dementia. The proposed model leverages the power of deep learning techniques to automatically learn relevant features from the data and effectively distinguish between healthy individuals and those with FTD. The experimental results demonstrate the promising potential of the proposed approach in improving FTD diagnosis and paving the way for future research in this area.