Can I read Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform on EtoBox?

Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform by Elmira Amooei; Arash Sharifi; Mohammad Manthouri is a Computer Science article available to read on EtoBox.

What is Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform about?

Early diagnosis of neurodegenerative diseases has always been a major challenge that physicians and medical practitioners face. Therefore, using any method or device that helps with prognostics is of great importance. In recent years, deep neural networks have become popular in medical fields, and the reason is that these networks can help diagnose diseases quickly and precisely. In this research, two novel models based on a CNN-LSTM network are introduced. The main goal is to classify three neurodegenerative diseases, including ALS, Parkinson's disease, and Huntington's disease, from one another and from healthy control patients using the gait signals, which are transformed into spectrogram images. In the first model, the spectrogram images derived from the gait signals are fed into a CNN-LSTM network directly. This model achieved 99.42% accuracy. In the second model, the same input data was used to be classified using a CNN-LSTM network, which uses wavelet transform as a feature extractor before the LSTM unit. During the experiments with the second model, the detail sub-bands were eliminated one by one, and the classification results were compared. Comparing these two models has

Who reads Early Diagnosis of Neurodegenerative Diseases Using CNN-LSTM and Wavelet Transform?

Author

Elmira Amooei; Arash Sharifi; Mohammad Manthouri

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Computer Science (Physical Sciences)