Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

Janghel, Rekh Ram; Rathore, Yogesh

doi:10.1016/j.irbm.2020.06.006

Cited by 124 publications

(53 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method was applied on the 53 subjects, including 27 Alzheimer’s patients, which provides an accuracy of 96.23%. Janghel et al ( 2020 ) used a convolution neural network to improve classification accuracy. They demonstrated a deep learning technique for identifying Alzheimer’s disease using data from the Alzheimer’s disease neuroimaging initiative database, which included magnetic resonance imaging and positron emission tomography scan pictures of Alzheimer’s patients, as well as an image of a healthy individual.…”

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

483

173

View full text Add to dashboard Cite

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

show abstract

Section: Reported Workmentioning

confidence: 99%

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Kumar

Koul

Singla³

et al. 2022

J Ambient Intell Human Comput

483

173

View full text Add to dashboard Cite

show abstract

“…Shankar K et al [23] uses the grey wolf optimization technique with a decision tree, KNN, and CNN model to diagnose AD and achieve a 96.23 % accuracy. R.R Janghel et al [24] proposed a pretrained VGG16 to extract the features of AD from the ADNI database. For classification, they used SVM, Linear Discriminate, K means clustering and decision tree algorithm.…”

Section: Related Workmentioning

confidence: 99%

DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

et al. 2021

View full text Add to dashboard Cite

Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classify cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconstant due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) utilized to create a framework that can exploit to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) a CNN model is proposed to detect the dementia stages, which is the primary cause of AD. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.

show abstract

“…Third, a solitary hidden layer neural network organization was utilized as the classifier and the model recorded an accuracy of 92.71% for the detection of Alzheimer's disease. Rekh Ram Janghel et al proposed a unique method to increase the performance of CNN architecture by applying some pre-processing in the dataset before sending the dataset to extract features, the method has achieved an average accuracy of about 99.45% on fMRI data [14]. Khagi et al [15] proposed a method which performed classification of Alzheimer's disease based on transfer learning [TL] from various pre-trained CNN models and one scratch model, wherein the scratch model achieved greater accuracy of about 53.69% among various models, but it can be highly improved by tuning the parameters of scratch CNN model.…”

Section: Literature Reviewmentioning

confidence: 99%

Early Diagnosis of Alzheimer’s Disease Using ACO Optimized Deep CNN Classifier

Singh

Janghel

2021

Smart Innovation, Systems and Technologies

View full text Add to dashboard Cite

Alzheimer's disease (AD) is a human brain disease that remains as a common cause of dementia, which occurs mainly in middle-aged or grown-up individuals. AD results in cognitive decline and memory loss. AD is caused by the decomposition of plaques around the nerves of brains or around the brain cells, where the brain cells get neurofibrillary tangled and result in various instability and mental illness. AD is a chronic and irreversible disease; the reasons and disease identification are still not known, but research says it can be identified during the early stages. In spite of that fact, this research work has proposed a computer-aided Alzheimer's classification method that will classify the class of an image either in normal class or demented class. The method uses the hybrid strategy of ant colony optimization (ACO) and feed forward convolutional neural network (CNN or ConvNet); however, identifying the architecture of CNN requires lots of expertise and is time-consuming. Henceforth, this research work has used the bio-inspired optimization strategy, which will identify the optimal combination of hyper-parameters, i.e. it recommends the configuration for the CNN model, and with that, configuration of hyper-parameters with the CNN model is trained with the training dataset, and CNN performs feature extraction alongside classification for arranging the gatherings possibly, when the model undergoes validation, where the performance metric of the model is evaluated and to identify whether the validating images are falling in the category of normal class (i.e. non-demented) or Alzheimer's class (i.e. demented class) with good results or not, the classification error is measured during this phase and is backpropagated to ACO optimizer, iteratively ACO is used to minimize the classification error by tuning the hyper-parameters, and after few iterations, a CNN architecture with optimal hyper-parameters combinations is obtained to result in least classification errors. The method was applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which constitutes the fMRI images of Alzheimer's affected patients and resulted in developing efficient and state-of-the-art method for the classification of Alzheimer's disease. The proposed method performance metrics were recorded

show abstract

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

Cited by 124 publications

References 20 publications

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images

Early Diagnosis of Alzheimer’s Disease Using ACO Optimized Deep CNN Classifier

Contact Info

Product

Resources

About