Diagnosis of Alzheimer’s Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images

Pusparani, Yori; Lin, Chih-Yang; Jan, Yih‐Kuen; Lin, Fu-Yu; Liau, Ben-Yi; Ardhianto, Peter; Farady, Isack; Alex, John Sahaya Rani; Aparajeeta, Jeetashree; Chao, Wen-Hung; Lung, Chi-Wen

doi:10.1109/access.2023.3285115

Cited by 11 publications

(2 citation statements)

References 55 publications

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“…The annotation work was manually outlined by three medical experts, and then segmented and annotated by five computer annotators using Lableme. After the dataset was generated, it was finally reviewed by three medical experts to obtain the final dataset ( Pusparani et al, 2023 ). Another problem is the shape is irregular and the contrast with its surrounding tissue is not obvious.…”

Section: Methodsmentioning

confidence: 99%

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Zhang,

Cheng,

Zhou

et al. 2023

Front. Physiol.

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Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People’s Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).

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Section: Methodsmentioning

confidence: 99%

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Zhang,

Cheng,

Zhou

et al. 2023

Front. Physiol.

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show abstract

“…[ 15 ] developed a robust low-cost neural network classification system for AD and Mild Cognitive Impairment (MCI) against NC using a CNN with input images based on diffusion maps and gray-matter volumes, achieving competitive results of 93.5% for AD/NC classification. In another study relating raw MRI data [ 16 ], the authors employed ResNet-50 and LeNet on AD classification based on MRI slices in three views and categories. The study demonstrated that the selecting slices performed better than using entire slices in MRI images for AD classification and the coronal view showed higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer’s disease classification based on gradient-weighted class activation mapping

Song,

Yoshida

2024

PLoS ONE

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Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer’s Disease (AD), yet only a handful have provided results regarding explainability. We address this gap by applying several prevalent explainability methods such as gradient-weighted class activation mapping (Grad-CAM) to an fMRI-based 3D-VGG16 network for AD diagnosis to improve the model’s explainability. The aim is to explore the specific Region of Interest (ROI) of brain the model primarily focuses on when making predictions, as well as whether there are differences in these ROIs between AD and normal controls (NCs). First, we utilized multiple resting-state functional activity maps including ALFF, fALFF, ReHo, and VMHC to reduce the complexity of fMRI data, which differed from many studies that utilized raw fMRI data. Compared to methods utilizing raw fMRI data, this manual feature extraction approach may potentially alleviate the model’s burden. Subsequently, 3D-VGG16 were employed for AD classification, where the final fully connected layers were replaced with a Global Average Pooling (GAP) layer, aimed at mitigating overfitting while preserving spatial information within the feature maps. The model achieved a maximum of 96.4% accuracy on the test set. Finally, several 3D CAM methods were employed to interpret the models. In the explainability results of the models with relatively high accuracy, the highlighted ROIs were primarily located in the precuneus and the hippocampus for AD subjects, while the models focused on the entire brain for NC. This supports current research on ROIs involved in AD. We believe that explaining deep learning models would not only provide support for existing research on brain disorders, but also offer important referential recommendations for the study of currently unknown etiologies.

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A method for cabbage root posture recognition based on YOLOv5s

Qiu,

Shao,

Zhou

et al. 2024

Heliyon

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Diagnosis of Alzheimer’s Disease Using Convolutional Neural Network With Select Slices by Landmark on Hippocampus in MRI Images

Cited by 11 publications

References 55 publications

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer’s disease classification based on gradient-weighted class activation mapping

A method for cabbage root posture recognition based on YOLOv5s

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