Deep Learning for Predictive Forecasts and Brain Imaging in Memory Loss Diseases
P. Arunachalam, Vaibhav C. Gandhi, Jalpa Bhatt, Tushar Desai, S. Surendran, T J Nandhini
Abstract
Because deep learning is so adept at finding patterns in complex, high-dimensional data, it has revolutionized computer vision, beating traditional machine learning models. The development of brain imaging technology led to extensive multim…ORIZATIONS: Deep learning algorithms, Alzheimer's illness, and classification This chapter presents a review of recent neuroimaging studies that used deep learning techniques to enhance AD classification and evaluation. Articles Published between January 2013 and July 2018 (searched on Pubmed and Google Scholar) Alzheimer's Disease Deep Learning Following a review of the articles, the findings were rated based on the classification technique and the kind of neuroimaging data utilized in each research. Twelve of the sixteen included research exclusively used deep learning models (CNN for eleven [5] – [14] and Bidirectional Long Short-Term Memory networks—Bi-LSTM for one study); the other four studies mixed classical machine learning with deep learning techniques [1] - [4]. The best accurate diagnosis result was obtained when these multimodal techniques were combined with biomarkers found in cerebrospinal fluid (used for further investigations). Deep learning algorithms have a lot of promise for the diagnostic criteria used to classify AD since they are getting more efficient. Understanding AD is still in its early stages, but deep learning and explainable technologies like these may help shed more light on the symptoms of the illness.