Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease

Sharma, Sarang; Gupta, Sheifali; Gupta, Deepali; Juneja, Sapna; Mahmoud, Amena; El–Sappagh, Shaker; Kwak, Kyung Sup

doi:10.3389/fncom.2022.1000435

Cited by 12 publications

(4 citation statements)

References 30 publications

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“…Their work obtained 94.93%, 94.94%, 98.3%, and 94.92% precision, recall, specificity, and accuracy, respectively. Without any details, the authors stated that the work could not guarantee reproducibility 15 .…”

Section: Labeling Dementia Stagesmentioning

confidence: 99%

Histogram-based features track Alzheimer's progression in brain MRI

Pasnoori,

Flores-Garcia,

Barkana

2024

Sci Rep

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Alzheimer's disease is a form of general dementia marked by amyloid plaques, neurofibrillary tangles, and neuron degeneration. The disease has no cure, and early detection is critical in improving patient outcomes. Magnetic resonance imaging (MRI) is important in measuring neurodegeneration during the disease. Computer-aided image processing tools have been used to aid medical professionals in ascertaining a diagnosis of Alzheimer's in its early stages. As characteristics of non and very-mild dementia stages overlap, tracking the progression is challenging. Our work developed an adaptive multi-thresholding algorithm based on the morphology of the smoothed histogram to define features identifying neurodegeneration and track its progression as non, very mild, mild, and moderate. Gray and white matter volume, statistical moments, multi-thresholds, shrinkage, gray-to-white matter ratio, and three distance and angle values are mathematically derived. Decision tree, discriminant analysis, Naïve Bayes, SVM, KNN, ensemble, and neural network classifiers are designed to evaluate the proposed methodology with the performance metrics accuracy, recall, specificity, precision, F1 score, Matthew’s correlation coefficient, and Kappa values. Experimental results showed that the proposed features successfully label the neurodegeneration stages.

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Section: Labeling Dementia Stagesmentioning

confidence: 99%

Histogram-based features track Alzheimer's progression in brain MRI

Pasnoori,

Flores-Garcia,

Barkana

2024

Sci Rep

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“…CNNs have also been extended with additional layers to make the model deeper to classify pulmonary nodules [21]. A shallow version of the Inception model was used to diagnose Alzheimer's disease [22]. Deeper and wider CNNs have been evaluated for a variety of computer-aided detection algorithms with transfer learning [23].…”

Section: Transfer Learningmentioning

confidence: 99%

Maintaining Symmetry between Convolutional Neural Network Accuracy and Performance on an Edge TPU with a Focus on Transfer Learning Adjustments

DeLozier,

Blanco,

Rakvic

et al. 2024

Symmetry

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Transfer learning has proven to be a valuable technique for deploying machine learning models on edge devices and embedded systems. By leveraging pre-trained models and fine-tuning them on specific tasks, practitioners can effectively adapt existing models to the constraints and requirements of their application. In the process of adapting an existing model, a practitioner may make adjustments to the model architecture, including the input layers, output layers, and intermediate layers. Practitioners must be able to understand whether the modifications to the model will be symmetrical or asymmetrical with respect to the performance. In this study, we examine the effects of these adjustments on the runtime and energy performance of an edge processor performing inferences. Based on our observations, we make recommendations for how to adjust convolutional neural networks during transfer learning to maintain symmetry between the accuracy of the model and its runtime performance. We observe that the edge TPU is generally more efficient than a CPU at performing inferences on convolutional neural networks, and continues to outperform a CPU as the depth and width of the convolutional network increases. We explore multiple strategies for adjusting the input and output layers of an existing model and demonstrate important performance cliffs for practitioners to consider when modifying a convolutional neural network model.

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“…The skip connection links straight to the output after skipping a few stages of training. The benefit of including this type of skip connection is that regularization will skip any layer that degrades the architecture's performance [65] As a result, an intense neural network can be trained without the issues caused by vanishing/exploding gradients. ResNet152 [52] is a 152-layer CNN, and its modified architecture is displayed in Figure 4.…”

Section: Architecture Of Resnet152mentioning

confidence: 99%

An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images

Aggarwal

Gupta

et al. 2023

Sustainability

Self Cite

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Predicting subcellular protein localization has become a popular topic due to its utility in understanding disease mechanisms and developing innovative drugs. With the rapid advancement of automated microscopic imaging technology, approaches using bio-images for protein subcellular localization have gained a lot of interest. The Human Protein Atlas (HPA) project is a macro-initiative that aims to map the human proteome utilizing antibody-based proteomics and related c. Millions of images have been tagged with single or multiple labels in the HPA database. However, fewer techniques for predicting the location of proteins have been devised, with the majority of them relying on automatic single-label classification. As a result, there is a need for an automatic and sustainable system capable of multi-label classification of the HPA database. Deep learning presents a potential option for automatic labeling of protein’s subcellular localization, given the vast image number generated by high-content microscopy and the fact that manual labeling is both time-consuming and error-prone. Hence, this research aims to use an ensemble technique for the improvement in the performance of existing state-of-art convolutional neural networks and pretrained models were applied; finally, a stacked ensemble-based deep learning model was presented, which delivers a more reliable and robust classifier. The F1-score, precision, and recall have been used for the evaluation of the proposed model’s efficiency. In addition, a comparison of existing deep learning approaches has been conducted with respect to the proposed method. The results show the proposed ensemble strategy performed exponentially well on the multi-label classification of Human Protein Atlas images, with recall, precision, and F1-score of 0.70, 0.72, and 0.71, respectively.

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Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease

Cited by 12 publications

References 30 publications

Histogram-based features track Alzheimer's progression in brain MRI

Histogram-based features track Alzheimer's progression in brain MRI

Maintaining Symmetry between Convolutional Neural Network Accuracy and Performance on an Edge TPU with a Focus on Transfer Learning Adjustments

An Artificial Intelligence-Based Stacked Ensemble Approach for Prediction of Protein Subcellular Localization in Confocal Microscopy Images

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