Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease - Sharma, Sarang; Gupta, Sheifali; Gupta, Deepali; Juneja, Sapna; Mahmoud, Amena; El–Sappagh, Shaker; Kwak, Kyung-Sup (Frontiers in Computational Neuroscience, 2022)
Alzheimer’s Disease (AD) is a neurodegenerative ailment. This ailment sluggishly eradicates memory and gradationally weakens the introductory cognitive functions and capacities of the body similar as allowing, flashing back and logic. Thus, to diagnose this complaint CT, MRI, PET, etc. are enforced. Still, these ways are time consuming and occasionally yield inaccurate results. Thus, to avoid similar lengthy and time-consuming ways, deep learning models are enforced that are lower time consuming, yield results with lesser accuracy and could be enforced with ease. This paper proposes transfer literacy grounded modified Inception model with pre-processing ways of normalization and data addition. The proposed model achieved the accuracy of 94.92 and sensitivity of 94.94. It's concluded from the results that proposed model performs better as compared to other state- of- art models. For training purpose, the dataset has been taken from the Kaggle having 6200 images with 896 Mild Demented (M.D) images, 64 Moderate Demented (Mod.D) images, 3200 Non-Demented (N.D) images and 1966 veritably Mild Demented (V.M.D) images. With similar results these models could be employed for developing clinically useful results that are suitable to descry announcement in MRI images.