A Transfer Learning Approach for Neurodegenerative Disease Classification from Brain MRI Images: Distinguishing Alzheimer's, Parkinson's, and Control Cases DOI
Ayesha Siddiqua, Atib Mohammad Oni, Md. Jarez Miah

et al.

Published: May 2, 2024

Like many other critical medical conditions, different neurogenerative diseases, including Alzheimer's and Parkinson's need to get diagnosed in the primary stage. Deep learning algorithms show excellent performance detecting neurodegenerative diseases from images obtained by magnetic resonance imaging (MRI) of human brain. Recently, transfer has also shown promising outcomes classification neurological conditions using brain MRI data. Here, we examine efficacy strategy utilizing four distinct CNN architectures, namely EfficientNetB0, ResNet50, InceptionV3,and Xception. Used dataset for study three classes; disease (PD), (AD), control (healthy). The compares accuracy, precision, recall, F1- score metrics investigated models. result demonstrates that EfficientNetB0 model shows best training testing reaching an accuracy as high 99.4%.

Language: Английский

Comparison Between Explainable AI Algorithms for Alzheimer’s Disease Prediction Using EfficientNet Models DOI
Sobhana Jahan, Md. Rawnak Saif Adib, Mufti Mahmud

et al.

Lecture notes in computer science, Journal Year: 2023, Volume and Issue: unknown, P. 357 - 368

Published: Jan. 1, 2023

Language: Английский

Citations

12
Deep learning in pediatric neuroimaging DOI Creative Commons
Jian Wang, Jiaji Wang, Shuihua Wang‎

et al.

Displays, Journal Year: 2023, Volume and Issue: 80, P. 102583 - 102583

Published: Nov. 15, 2023

The integration of deep learning techniques in pediatric neuroimaging has shown significant promise advancing various aspects the field. This paper provides a comprehensive exploration applications neuroimaging, focusing on image processing and reconstruction, segmentation classification, brain abnormalities detection, development maturation analysis. It discusses key deep-learning their relevance neuroimaging. also addresses challenges limitations such as lack standardization, ethical privacy concerns, limited heterogeneous data, age, gender, developmental variations. highlights future directions opportunities, including multi-modal considerations, diagnosing initiating treatment during early stages, impact maternal emotional well-being development. insights provided this aim to contribute understanding how can positively inspire further research innovation Ultimately, adopting improve patient outcomes, advance diagnostic accuracy, enhance our

Language: Английский

Citations

12
A Comprehensive Analysis of Deep Learning-Based Approaches for the Prediction of Gastrointestinal Diseases Using Multi-class Endoscopy Images DOI
Priya Bhardwaj,

Sanjeev Kumar,

Yogesh Kumar

et al.

Archives of Computational Methods in Engineering, Journal Year: 2023, Volume and Issue: 30(7), P. 4499 - 4516

Published: June 15, 2023

Language: Английский

Citations

11
Comparative evaluation of deep learning techniques for multistage Alzheimer's prediction from magnetic resonance images DOI
Pushpendra Gupta,

Pradeep Nahak,

Vidyapati Kumar

et al.

Elsevier eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 135 - 151

Published: Jan. 1, 2025

Language: Английский

Citations

0
Optimized Alzheimer disorder classification with DACN-MFFN utilizing OBLDE-TDO enhanced deep neural network features DOI

M. Karthiga,

E. Suganya,

S. Sountharrajan

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 106, P. 107729 - 107729

Published: March 1, 2025

Language: Английский

Citations

0
Deep learning-based classification of dementia using image representation of subcortical signals DOI Creative Commons

Shivani Ranjan,

Ayush Tripathi,

Harshal Shende

et al.

BMC Medical Informatics and Decision Making, Journal Year: 2025, Volume and Issue: 25(1)

Published: March 6, 2025

Dementia is a neurological syndrome marked by cognitive decline. Alzheimer's disease (AD) and frontotemporal dementia (FTD) are the common forms of dementia, each with distinct progression patterns. Early accurate diagnosis cases (AD FTD) crucial for effective medical care, as both conditions have similar early-symptoms. EEG, non-invasive tool recording brain activity, has shown potential in distinguishing AD from FTD mild impairment (MCI). This study aims to develop deep learning-based classification system analyzing EEG derived scout time-series signals regions, specifically hippocampus, amygdala, thalamus. Scout time series extracted via standardized low-resolution electromagnetic tomography (sLORETA) technique utilized. The converted image representations using continuous wavelet transform (CWT) fed input learning models. Two high-density datasets utilized validate efficacy proposed method: online BrainLat dataset (128 channels, comprising 16 AD, 13 FTD, 19 healthy controls (HC)) in-house IITD-AIIA (64 including subjects 10 9 MCI, 8 HC). Different strategies classifier combinations been mapping classes data sets. best results were achieved product probabilities classifiers left right subcortical regions conjunction DenseNet model architecture. It yield accuracies 94.17 % 77.72 on datasets, respectively. highlight that representation-based approach differentiate various stages dementia. pave way more early diagnosis, which treatment management debilitating conditions.

Language: Английский

Citations

0
A Comprehensive Study of Deep Learning Methods for Kidney Tumor, Cyst, and Stone Diagnostics and Detection Using CT Images DOI
Yogesh Kumar, Tejinder Pal Singh Brar,

Chhinder Kaur

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: May 9, 2024

Language: Английский

Citations

3
Detection of Alzheimer's disease using deep learning models: A systematic literature review DOI Creative Commons

Eqtidar M. Mohammed,

Ahmed M. Fakhrudeen, Omar Alani

et al.

Informatics in Medicine Unlocked, Journal Year: 2024, Volume and Issue: 50, P. 101551 - 101551

Published: Jan. 1, 2024

Alzheimer's disease (AD) is a progressive neurological considered the most common form of late-stage dementia. Usually, AD leads to reduction in brain volume, impacting various functions. This article comprehensively analyzes context fivefold main topics. Firstly, it reviews imaging techniques used diagnosing disease. Secondly, explores proposed deep learning (DL) algorithms for detecting Thirdly, investigates commonly datasets develop DL techniques. Fourthly, we conducted systematic review and selected 45 papers published highly ranked publishers (Science Direct, IEEE, Springer, MDPI). We analyzed them thoroughly by delving into stages diagnosis emphasizing role preprocessing Lastly, paper addresses remaining practical implications challenges context. Building on analysis, this survey contributes covering several aspects related that have not been studied thoroughly.

Language: Английский

Citations

3
Machine Learning-Based Approaches for the Prognosis and Prediction of Multiple Diseases DOI
Priya Bhardwaj, Yogesh Kumar, Shakti Mishra

et al.

Published: Jan. 24, 2024

The rapid progress in machine learning techniques has significantly transformed healthcare which enables the simultaneous and accurate detection of multiple diseases. This paper delves into application diverse algorithms for multi-disease by using a comprehensive dataset focuses on three diseases i.e. diabetes, gonorrhoea, typhoid. been meticulously pre-processed graphically visualized to discern patterns represent against emotional states/urges critical feelings. Subsequently, range classifiers includes logistic regression, Adaboost, random forest, support vector machine, CatBoost, Light Gradient Boosting Classifier, Naïve Bayes, XGBoost, KNN, Decision Tree, are trained this dataset. Their performance across these different classes is rigorously evaluated various parameters such as accuracy, F1 score, recall, precision. During execution, Adaboost emerged top performer, achieving an impressive accuracy 94.37% maintaining precision, score 0.94, indicates its robustness detection.

Language: Английский

Citations

2
Comprehensive Systematic Computation on Alzheimer's Disease Classification DOI
Prashant Upadhyay, Pradeep Tomar, Satya Prakash Yadav

et al.

Archives of Computational Methods in Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: April 30, 2024

Language: Английский

Citations

2