lyzy-cai-czh

lyzy-cai-czh's Stars

• chanchanchan97/ICAFusion

  ICAFusion: Iterative Cross-Attention Guided Feature Fusion for Multispectral Object Detection, Pattern Recognition

  Language:Python1449

• WyZhuNUAA/DA-MIDL

  Dual Attention Multi-Instance Deep Learning for AD diagnosis

  Language:Python341

• jzplp/Cpp-Primer-Answer

  C++ Primer 第五版中文版 习题答案代码

  Language:C++4114

• Knight825/models-pytorch

  This is a torchvision style CNN models collection based on pytorch.

  Language:Python215

• aramis-lab/AD-DL

  Classification of Alzheimer's disease status with convolutional neural networks.

  Language:Python5610

• ZF4444/MMAL-Net

  This is a PyTorch implementation of the paper "Multi-branch and Multi-scale Attention Learning for Fine-Grained Visual Categorization (MMAL-Net)" (Fan Zhang, Meng Li, Guisheng Zhai, Yizhao Liu).

  Language:Python25257

• rsinghlab/MADDi

  This repository is for the Multimodal Alzheimer’s Disease Diagnosis framework (MADDi).

  Language:Jupyter Notebook8517

• shahidzikria/ADD-Net

  Alzheimer’s Disease (AD) is a neurological brain disorder marked by dementia and neurological dysfunction that affects memory, behavioral patterns, and reasoning. Alzheimer’s disease is an incurable disease that primarily affects people over the age of 40. Presently, Alzheimer’s disease is diagnosed through a manual evaluation of a patient’s MRI scan and neuro-psychological examinations. Deep Learning (DL), a type of Artificial Intelligence (AI), has pioneered new approaches to automate medical image diagnosis. The goal of this study is to create a reliable and efficient approach for classifying AD using MRI by applying the deep Convolutional Neural Network (CNN). In this paper, we propose a new CNN architecture for detecting AD with relatively few parameters and the proposed solution is ideal for training a smaller dataset. This proposed model successfully distinguishes the early stages of Alzheimer’s disease and shows class activation maps as a heat map on the brain. The proposed Alzheimer’s Disease Detection Network (ADD-Net) is built from scratch to precisely classify the stages of AD by decreasing parameters and calculation costs. The Kaggle MRI image dataset has a significant class imbalance problem and we exploited a synthetic oversampling technique to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed ADD-Net is extensively evaluated against DenseNet169, VGG19, and InceptionResNet V2 using precision, recall, F1-score, Area Under the Curve (AUC), and loss. The ADD-Net achieved the following values for evaluation metrics: 98.63%, 99.76%, 98.61%, 98.63%, 98.58%, and 0.0549 for accuracy, AUC , F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed ADD-Net outperforms other state-of-the-art models in all the evaluation metrics.

  Language:Jupyter Notebook122

• PaulKMandal/Alzheimer-Detection

  Language:Jupyter Notebook22

• Fafa-DL/Lhy_Machine_Learning

  李宏毅2021/2022/2023春季机器学习课程课件及作业

  Language:Jupyter Notebook6.4k1.6k