Prediction of Stage of Alzheimer's Disease DenseNet Deep Learning Model

Rathore, Yogesh; Janghel, Rekh Ram

doi:10.1201/9781003217091-8

Cited by 3 publications

(2 citation statements)

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“…In this paper, the dense block is selected as the model base architecture. The reason is that dense block can effectively alleviate the problem of model gradient vanishing [ 35 , 36 , 37 ], making backpropagation easier and the model convergence effect better. FC-DCN retains important features more comprehensively from the initial layer to the final output through feature reuse.…”

Section: Discussionmentioning

confidence: 99%

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

et al. 2022

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According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator’s technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model’s accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.

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Section: Discussionmentioning

confidence: 99%

Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

et al. 2022

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“…Cheng et al [ 30 ] proposed a modular group attention block that captures feature dependencies in medical images in both channel and spatial dimensions and stacked these group attention blocks in the ResNet style to improve model classification performance. Extensive experiments by Rathore et al [ 31 ] on ADNI [ 32 ] dataset showed that the DenseNet model improved classification accuracy by about 9% compared to traditional machine learning, which proved the usefulness of the DenseNet model. Kong and Cheng [ 33 ] fused DenseNet and VGG, introduced an attention mechanism (global attention machine block and category attention block) to extract depth features and used ResNet to segment effective image information to achieve fast and accurate classification.…”

Section: Related Workmentioning

confidence: 99%