Intelligent Alzheimer's Detector Using Deep Learning

Puranik, Mukul; Shah, Himanshu; Shah, Keval; Bagul, Sudhir

doi:10.1109/iccons.2018.8663065

Cited by 14 publications

(9 citation statements)

References 4 publications

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“…Around 53% of studies attempted to offer a model that can classify MCI and normal controls from AD. Classification of AD, MCI, and normal controls was also studied extensively (Khan et al, 2019 ; Puranik et al, 2019 ; Li Y et al, 2021 ; Yang and Hong, 2021 ). Detection (Choi et al, 2020 ), Classification (Cheng et al, 2017 ), and autoencoder (Oh et al, 2019 ) methods were utilized to differentiate MCI from AD and/or normal controls.…”

Section: Resultsmentioning

confidence: 99%

“…finetuned convolution and FC layers for AlexNet and GoogLeNet architectures pre-trained on ImageNet dataset. Several classifiers were added to the end of FC layers and obtained an accuracy of 88-100% for different classifiers such as KNN, Naïve Bayes, SVM, and Logistic Regression on top of AlexNet and GoogLeNet Liang et al (2018),Eitel et al (2019),Khan et al (2019),Oh et al (2019),Puranik et al (2019),Ramzan et al (2020),Simon et al (2019),Wu et al (2019),Zhang et al (2021), andOcasio andDuong (2021). also utilized this approach and obtained competitive results.…”

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confidence: 99%

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Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Ardalan

Subbian

2022

Front. Artif. Intell.

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Deep learning algorithms have been moderately successful in diagnoses of diseases by analyzing medical images especially through neuroimaging that is rich in annotated data. Transfer learning methods have demonstrated strong performance in tackling annotated data. It utilizes and transfers knowledge learned from a source domain to target domain even when the dataset is small. There are multiple approaches to transfer learning that result in a range of performance estimates in diagnosis, detection, and classification of clinical problems. Therefore, in this paper, we reviewed transfer learning approaches, their design attributes, and their applications to neuroimaging problems. We reviewed two main literature databases and included the most relevant studies using predefined inclusion criteria. Among 50 reviewed studies, more than half of them are on transfer learning for Alzheimer's disease. Brain mapping and brain tumor detection were second and third most discussed research problems, respectively. The most common source dataset for transfer learning was ImageNet, which is not a neuroimaging dataset. This suggests that the majority of studies preferred pre-trained models instead of training their own model on a neuroimaging dataset. Although, about one third of studies designed their own architecture, most studies used existing Convolutional Neural Network architectures. Magnetic Resonance Imaging was the most common imaging modality. In almost all studies, transfer learning contributed to better performance in diagnosis, classification, segmentation of different neuroimaging diseases and problems, than methods without transfer learning. Among different transfer learning approaches, fine-tuning all convolutional and fully-connected layers approach and freezing convolutional layers and fine-tuning fully-connected layers approach demonstrated superior performance in terms of accuracy. These recent transfer learning approaches not only show great performance but also require less computational resources and time.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Ardalan

Subbian

2022

Front. Artif. Intell.

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“…Due to the small sample size of medical images, deep-learning-based diagnosis methods suffer many limitations. In paper (Puranik et al, 2018 ; Taheri and Naima, 2019 ), the classification accuracy is achieved by using 2D slices of neuroimaging data as input of the CNN, which is based on slice-level classification. In order to acquire the subject-level classification accuracy, we integrate all slice features of each subject.…”

Section: Discussionmentioning

confidence: 99%

“…AD and LMCI vs. EMCI, but the classification task excluded EMCI vs. NC. Puranik et al ( 2018 ) employed a 2DCNN model with transfer learning technique to classify AD, EMCI, and NC and obtained an accuracy of 98.41%. However, the inputs of the CNN are the 2D slices of fMRI images, which means that the classification task is not based on subject-level, deviating clinical needs.…”

Section: Introductionmentioning

confidence: 99%

Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning

Kang

Jiang

Huang

et al. 2020

Front. Aging Neurosci.

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“…Over the past decade, deep learning techniques have enabled medical imaging scientists to predict various stages of Alzheimer's disease [19], [20]. Using robust computational resources such as cloud computing, the scientists could implement end-to-end prediction pipelines to preprocess medical imaging data, build complex deep learning models, and post-process results to assist medical doctors to distinguish early-stage MCI brains from highly correlated normal aging images [21]- [24]. Convolutional neural networks (CNNs) inspired by the human visual system form such pipelines' core image classification component.…”

Section: Introductionmentioning

confidence: 99%

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

Sarraf¹,

Sarraf

DeSouza

et al. 2021

Preprint

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Advances in applied machine learning techniques to neuroimaging have encouraged scientists to implement models to early diagnose brain disorders such as Alzheimer’s Disease. Predicting various stages of Alzheimer’s disease is challenging; however, existing deep learning complex techniques could perform such a prediction. Therefore, using novel architectures with less complexity but efficient pattern extraction capabilities such as transformers has been of interest to neuroscientists. This study introduced an optimized vision transformer architecture to predict the aging effect in healthy adults (>75 years), mild cognitive impairment, and Alzheimer’s’ brains within the same age group using resting-state functional and anatomical magnetic resonance imaging data. Our optimized architecture known as OViTAD, which is currently the sole vision transformer-based end-to-end pipeline, outperformed the existing transformer models and most state-of-the-art solutions with F1-scores of 97%±0.0 and 99.55%±0.39 achieved from the testing sets for the two modalities in the triple-class prediction experiments where the number of trainable parameters decreased by 30% compared to a vanilla transformer. To ensure the robustness and reproducibility of our optimized vision transformer, we repeated the modeling process three times for all the experiments and reported the averaged evaluation metrics. Furthermore, we implemented a visualization technique to illustrate the effect of global attention on brain images. Also, we exhaustively implemented models to explore the impact of combining healthy brains with two other groups in the two modalities. This study could open a new avenue of adopting and optimizing vision transformers for neuroimaging applications, especially for Alzheimer’s Disease prediction.

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Intelligent Alzheimer's Detector Using Deep Learning

Cited by 14 publications

References 4 publications

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Transfer Learning Approaches for Neuroimaging Analysis: A Scoping Review

Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning

OViTAD: Optimized Vision Transformer to Predict Various Stages of Alzheimer's Disease Using Resting-State fMRI and Structural MRI Data

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