An accurate Alzheimer's disease detection using a developed convolutional neural network model

Abstract

Alzheimer's disease indicates one of the highest difficult to heal diseases, and it is acutely affecting the elderly normal lives and their households. Early, effective, and accurate detection represents an important blueprint for minimizing Alzheimer's progression risk. The modalities of brain imaging can assist in identifying the abnormalities associated with Alzheimer's disease. This research presents a developed deep learning scheme, which is designed and implemented to classify the brain images into multiclass, namely very mild, moderate, mild, and non-demented. The proposed convolutional neural network (CNN) based detection model attained a high performance with an accuracy of 99.92%, considerably enhancing the results achieved via the pre-trained 16 layers in the visual geometric group (VGG16) model and the other related learning models. Consequently, this developed model can assist medical personnel by providing a facilitating tool to identify Alzheimer's disease stage and establishing a suitable medical treatment platform.

Text prediction recurrent neural networks using long short-term memory-dropout

“…Lin et al [18] developed a forecasting model with LSTM using decomposed data to obtain the prediction sequence to forecast the stock index price of the standard & poor's 500 indexes [19] and used the LSTM neural network model with the Adam algorithm to achieve better prediction results. So well in medicine convolutional neural networks are used for data processing, allowing to generate a prediction in time of a certain disease, as in the case of alzheimer's disease, diabetes, covid, cancer, and Parkinson [20]- [24].…”

Image captioning to aid blind and visually impaired outdoor navigation

“…The CNN on the encoder, like in the classification task, can be built from scratch or using a pre-trained model. Transfer learning is a model that was previously trained on a large dataset and then reused (with parameter adjustments) as a starting point for other tasks with a new dataset [18], [48]- [50]. The captioning task in [51], [52] was trained using three pre-trained models: visual geometry group (VGG) and ResNet.…”