Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

Solano-Rojas, Braulio J.; Villalón-Fonseca, Ricardo; Marín-Raventós, Gabriela

doi:10.1007/978-3-030-51517-1_1

Cited by 33 publications

(22 citation statements)

References 13 publications

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“…As we introduced in Section 5 , we trained our final model up to 110 epochs. We extended what we previously undertook in [ 8 ]. We evaluated the training to 110 epochs with more rigor.…”

Section: Discussionmentioning

confidence: 85%

“…Previously, in [ 8 ], we selected by title, and if the title was cryptic, we also used the abstract. We focused on the implementation of convolutional neural networks.…”

Section: Previous Workmentioning

confidence: 99%

“…As stated in [ 8 ], we identified a concern when analyzing the collected papers and their data. Approximately 50% of papers report accuracy, but did not report sensitivity, specificity, or AUROC.…”

Section: Previous Workmentioning

confidence: 99%

“…In [ 8 ], we created a three-dimensional CNN architecture, specifically DenseNet-121, trained using the ADNI MRI image dataset. We measured the accuracy of detecting Alzheimer’s disease with the implemented DNN architecture.…”

Section: Introductionmentioning

confidence: 99%

“…As in [ 8 ], we kept our low-cost approach. Our goal is to provide a technological artifact that can be used across many health care services to benefit people, particularly for developing countries with difficulties accessing specialized computing platforms.…”

Section: Introductionmentioning

confidence: 99%

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A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Solano-Rojas

Ricardo

2021

Sensors

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Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86¯ mean accuracy, 0.86¯ mean sensitivity (micro-average), 0.86¯ mean specificity (micro-average), and 0.91¯ area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.

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“…As we introduced in Section 5 , we trained our final model up to 110 epochs. We extended what we previously undertook in [ 8 ]. We evaluated the training to 110 epochs with more rigor.…”

Section: Discussionmentioning

confidence: 85%

“…Previously, in [ 8 ], we selected by title, and if the title was cryptic, we also used the abstract. We focused on the implementation of convolutional neural networks.…”

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Solano-Rojas

Ricardo

2021

Sensors

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COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

Nazir

Nawaz

Javed

et al. 2022

Microscopy Res & Technique

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The COVID‐19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID‐19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID‐19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID‐19‐affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID‐19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre‐processing step, we introduced the DenseNet‐77 based UNET approach. Initially, the DenseNet‐77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID‐19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet‐77 framework as an end‐to‐end network to classify the input images into three classes namely healthy, COVID‐19‐affected, and pneumonia images. We evaluated the proposed model over the COVID‐19‐20 and COVIDx CT‐2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross‐dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID‐19 segmentation and classification and can accurately predict the infection growth in a certain time frame.

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A Predictive Deep Learning Ensemble-Based Approach for Advanced Cancer Classification

Kansal,

Sharma

2024

Communications in Computer and Information Science

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Alzheimer’s Disease Early Detection Using a Low Cost Three-Dimensional Densenet-121 Architecture

Cited by 33 publications

References 13 publications

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

A Predictive Deep Learning Ensemble-Based Approach for Advanced Cancer Classification

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