An Intelligent Framework for Alzheimer's disease Classification Using EfficientNet Transfer Learning Model

Sethi, Monika; Ahuja, Sachin; Singh, Sehajpreet; Verma, Jyoti; Chawla, Mukesh

doi:10.1109/esci53509.2022.9758195

Cited by 10 publications

(3 citation statements)

References 10 publications

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“…In this study, a pre-trained CNN model known as the EfficientNet model will be utilized for the multi-stage classification task after the MRI dataset is expanded using the GAN model. By efficiently adjusting the network's size and resolutions, the EfficientNet model can achieve better accuracy with fewer computational resources than other models [25]. As shown in Figure 4, its model structure is mainly built from convolutional blocks, especially mobile inverted bottleneck convolutional units (MBConv), which leads to its improved efficiency, thus explaining why the EfficientNet is chosen to be used as the classification model in this study [26].…”

Section: Pre-trained Cnn Classification Modelmentioning

confidence: 99%

Performance Enhancement of Alzheimer's Disease Diagnosis Using Generative Adversarial Network

Wong Pui Ching,

Shahrum Shah Abdullah,

Mohd Ibrahim Shapiai

et al. 2024

ARASET

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Insufficient medical images have always been a challenge for deep learning-based Alzheimer's disease classification and detection tasks. The availability of Magnetic Resonance Imaging (MRI) data is limited due to patients' privacy issues. Access to individual medical records is strongly protected by the law, and appropriate consent is needed to utilize them for research purposes. Besides that, publicly available databases often experience imbalanced classes, further contributing to the issue of insufficient data. Thus, the performance of deep learning models used to diagnose Alzheimer's disease is often hindered by this issue. Basic data augmentation methods such as geometrical augmentation techniques also had limited applications to medical data. Hence, this study proposes a deep learning technique of Generative Adversarial Network (GAN) to expand the MRI dataset and improve the classification model performance. MRI images from the Open Access Series of Imaging Studies (OASIS) database are used in this study to perform experiments and validate hypotheses. After applying GAN to expand the dataset, a pre-trained Convolutional Neural Network (CNN) model is used to classify the data into multiple classes of Alzheimer's and the model's performance is measured. As a result, an improvement in accuracy for the classification task can be observed, indicating that the GAN is a solution for overcoming the challenge of insufficient data for Alzheimer's diagnosis.

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Section: Pre-trained Cnn Classification Modelmentioning

confidence: 99%

Performance Enhancement of Alzheimer's Disease Diagnosis Using Generative Adversarial Network

Wong Pui Ching,

Shahrum Shah Abdullah,

Mohd Ibrahim Shapiai

et al. 2024

ARASET

View full text Add to dashboard Cite

show abstract

“…A typical CNN model performs a classification task by first taking an image as an input, assigning biases and learnable weights to various image elements before distinguishing them. However, the primary issue of CNN is the randomness in adjusting model parameters, such as the number of layers and neurons in each layer [24]. The EfficientNet model can overcome this issue by evenly modifying the network's parameters by employing a composite coefficient.…”

Section: Proposed Modelmentioning

confidence: 99%

Transfer Learning for Alzheimer's Disease Diagnosis using EfficientNet-B0 Convolutional Neural Network

Wong Pui Ching,

Shahrum Shah Abdullah,

Mohd Ibrahim Shapiai

et al. 2023

ARASET

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Alzheimer's disease (AD) is an irreversible neurological disorder that causes the gradual decline of one's cognitive abilities, and thus, early detection is significant to slow down its deterioration. Magnetic resonance imaging (MRI) images have been commonly used to diagnose AD. Furthermore, deep learning techniques such as the Convolutional Neural Network (CNN) are utilized to assist the diagnosis due to the complexity of MRI's analysis. However, CNN models require large datasets for training and have a challenging nature for model optimization. Thus, Transfer Learning, an emerging method that can improve the performance of the deep learning model by eliminating the need for training from scratch, is introduced. This paper will propose a Transfer Learning-based EfficientNet-B0 model to classify MRI brain images for AD diagnosis. The MRI images are obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI) database; only axial-plane images are used in this study. As a result, the multi-class classification of MRI images into AD, MCI, and NC classes using a Transfer Learning-based model resulted in a training accuracy of 98.93% and a validation accuracy of 87.17%. These results evidenced the significance of Transfer Learning in improving model performance.

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“…In recent years, ANN, GAN, and other DL technologies have advanced swiftly and occasionally outpaced conventional ML. The use of DL in NM, referring to the physics part, includes disease diagnosis using PET [19], SPECT [20,21], imaging physics using PET [22], SPECT [23], image reconstruction using PET [24], SPECT [25], image denoising using PET [26,27], SPECT [28], image segmentation using PET [29], SPECT [30], and image classification using PET [31], SPECT [32]. Similar ideas are presented in [2,[33][34][35], where examples of specific ML capabilities include automated image segmentation, pre-analysis, and quantitation, radiomic feature extraction from image data, image reconstruction, case triage and reporting prioritization, research and data mining, and natural language processing.…”

Section: Introductionmentioning

confidence: 99%

Review of artificial intelligence clinical applications in Nuclear Medicine

Panagiotidis,

Papachristou,

Makridou

et al. 2023

Nuclear Medicine Communications

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This paper provides an in-depth analysis of the clinical applications of artificial intelligence (AI) in Nuclear Medicine, focusing on three key areas: neurology, cardiology, and oncology. Beginning with neurology, specifically Alzheimer’s disease and Parkinson’s disease, the paper examines reviews on diagnosis and treatment planning. The same pattern is followed in cardiology studies. In the final section on oncology, the paper explores the various AI applications in multiple cancer types, including lung, head and neck, lymphoma, and pancreatic cancer.

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An Intelligent Framework for Alzheimer's disease Classification Using EfficientNet Transfer Learning Model

Cited by 10 publications

References 10 publications

Performance Enhancement of Alzheimer's Disease Diagnosis Using Generative Adversarial Network

Performance Enhancement of Alzheimer's Disease Diagnosis Using Generative Adversarial Network

Transfer Learning for Alzheimer's Disease Diagnosis using EfficientNet-B0 Convolutional Neural Network

Review of artificial intelligence clinical applications in Nuclear Medicine

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