Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Neurodegenerative disorders present a current challenge for accurate diagnosis and for providing precise prognostic information. Alzheimer’s disease (AD) and Parkinson's disease (PD), may take several years to obtain a definitive diagnosis. Due to the increased aging population in developed countries, neurodegenerative diseases such as AD and PD have become more prevalent and thus new technologies and more accurate tests are needed to improve and accelerate the diagnostic procedure in the early stages of these diseases. Deep learning has shown significant promise in computer-assisted AD and PD diagnosis based on MRI with the widespread use of artificial intelligence in the medical domain. This article analyses and evaluates the effectiveness of existing Deep learning (DL)-based approaches to identify neurological illnesses using MRI data obtained using various modalities, including functional and structural MRI. Several current research issues are identified toward the conclusion, along with several potential future study directions

Classification of Alzheimer’s and Parkinson’s Disease Based on VGG19 Features with Batch Normalization

Dementia is a condition when thinking, reasoning and memory skills are lost and patients have emotional instability and personality changes. Researchers are looking into how the underlying disease processes that lead to various kinds of dementia begin and interact. Additionally, they keep researching the various diseases and conditions that cause dementia. Alzheimer’s and Parkinson's disease contribute to dementia development. Recently deep learning-based techniques have surpassed the performance of traditional algorithms in the field of machine vision, image detection, natural language handling, object detection, and medical image analysis. This study proposed a transfer learning-based model for Parkinson’s and Alzheimer’s disease classification from slices of MRI. Pretrained VGG19 with Batch normalization is used for feature extraction and the final dense (fully connected-FC) layers are fine-tuned to meet our requirements. The performance of the model is analyzed by varying hyperparameters. The proposed model outperformed other pre-trained CNN models by achieving an accuracy of 97.19%

Diagnosis of Parkinson's Disease Based on Voice Signals Using SHAP and Hard Voting Ensemble Method

Background and Objective: Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's, characterized by motor and non-motor symptoms. Developing a method to diagnose the condition in its beginning phases is essential because of the significant number of individuals afflicting with this illness. PD is typically identified using motor symptoms or other Neuroimaging techniques, such as DATSCAN and SPECT. These methods are expensive, time-consuming, and unavailable to the general public; furthermore, they are not very accurate. These constraints encouraged us to develop a novel technique using SHAP and Hard Voting Ensemble Method based on voice signals. Methods: In this article, we used Pearson Correlation Coefficients to understand the relationship between input features and the output, and finally, input features with high correlation were selected. These selected features were classified by the Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Gradient Boosting, and Bagging. Moreover, the Hard Voting Ensemble Method was determined based on the performance of the four classifiers. At the final stage, we proposed Shapley Additive exPlanations (SHAP) to rank the features according to their significance in diagnosing Parkinson's disease. Results and Conclusion: The proposed method achieved 85.42% accuracy, 84.94% F1-score, 86.77% precision, 87.62% specificity, and 83.20% sensitivity. The study's findings demonstrated that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases

Nonlinear Weighting Ensemble Learning Model to Diagnose Parkinson's Disease Using Multimodal Data

This work was supported by the FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto (B-TIC-586-UGR20); the MCIN/AEI/10.13039/501100011033/ and FEDER \Una manerade hacer Europa" under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion,Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18 and P20-00525 projects. Grant by F.J.M.M. RYC2021-030875-I funded by MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRTR. Work by D.C.B. is supported by the MCIN/AEI/FJC2021-048082-I Juan de la Cierva Formacion'. Work by J.E.A. is supported by Next Generation EU Fund through a Margarita Salas Grant, and work by C.J.M. is supported by Ministerio de Universidades under the FPU18/04902 grant.Parkinson's Disease (PD) is the second most prevalent neurodegenerative disorder among adults. Although its triggers are still not clear, they may be due to a combination of different types of biomarkers measured through medical imaging, metabolomics, proteomics or genetics, among others. In this context, we have proposed a Computer-Aided Diagnosis (CAD) system that combines structural and functional imaging data from subjects in Parkinson's Progression Markers Initiative dataset by means of an Ensemble Learning methodology trained to identify and penalize input sources with low classification rates and/or high-variability. This proposal improves results published in recent years and provides an accurate solution not only from the point of view of image preprocessing (including a comparison between different intensity preservation techniques), but also in terms of dimensionality reduction methods (Isomap). In addition, we have also introduced a bagging classification schema for scenarios with unbalanced data.As shown by our results, the CAD proposal is able to detect PD with 96.48% of balanced accuracy, and opens up the possibility of combining any number of input data sources relevant for PD.FEDER/Junta deAndalucia-Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades/Proyecto B-TIC-586-UGR20MCIN/AEI P20-00525FEDER \Una manerade hacer Europa RYC2021-030875-IJunta de AndaluciaEuropean Union (EU) Spanish Government RTI2018-098913-B100, CV20-45250, A-TIC-080-UGR18European Union (EU)Juan de la Cierva FormacionNext Generation EU Fund through a Margarita Salas GrantMinisterio de Universidades FPU18/0490

Quantitative Susceptibility Mapping in Cognitive Decline: A Review of Technical Aspects and Applications

In the human brain, essential iron molecules for proper neurological functioning exist in transferrin (tf) and ferritin (Fe3) forms. However, its unusual increment manifests iron overload, which reacts with hydrogen peroxide. This reaction will generate hydroxyl radicals, and irons higher oxidation states. Further, this reaction causes tissue damage or cognitive decline in the brain and also leads to neurodegenerative diseases. The susceptibility difference due to iron overload within the volume of interest (VOI) responsible for field perturbation of MRI and can benefit in estimating the neural disorder. The quantitative susceptibility mapping (QSM) technique can estimate susceptibility alteration and assist in quantifying the local tissue susceptibility differences. It has attracted many researchers and clinicians to diagnose and detect neural disorders such as Parkinsons, Alzheimers, Multiple Sclerosis, and aging. The paper presents a systematic review illustrating QSM fundamentals and its processing steps, including phase unwrapping, background field removal, and susceptibility inversion. Using QSM, the present work delivers novel predictive biomarkers for various neural disorders. It can strengthen new researchers fundamental knowledge and provides insight into its applicability for cognitive decline disclosure. The paper discusses the future scope of QSM processing stages and their applications in identifying new biomarkers for neural disorders

Brain neural network, development, microbiome, microbial toxins and COVID-19

Although almost 2 years have passed since the beginning of the coronavirus disease 2019 (COVID-19) pandemic in the world, there is still a threat to the health of people at risk and patients. Specialists in various sciences conduct various researches in order to eliminate or reduce the problems caused by this disease. Neural network science plays a vital role in this regard. It is important to note the key points of neuro-microbial involvement in the diagnosis and management of COVID-19 therapy by physicians and patients whose nervous systems are challenged. The relationship between COVID-19, microbiome and the profile of microbial toxins in the body is one of the factors that can directly or indirectly play a key role in the body's resistance to Covid-19 and changes in the neural network of the brain. In this article, we introduce the relationship and behavioral and mood problems that can result from neuronal changes. In linking the components of this network, artificial intelligence (AI), machine learning (ML) and data mining (DM) can be important strategies to assist health providers to choose best decision based on patient’s history.