使用迁移学习从胸部X射线图像中检测肺炎资源-CSDN文库

共28个文件

ipynb：19个

png：5个

license：1个

深度学习

图像识别

需积分: 42 12 浏览量 2022-09-09 15:46:23 上传评论 3 收藏 9.96MB ZIP 举报

资源详情

资源评论

资源推荐

收起资源包目录

Pneumonia-Detection-from-Chest-X-Ray-Images-with-Deep-Learning-master.zip （28个子文件）

Pneumonia-Detection-from-Chest-X-Ray-Images-with-Deep-Learning-master

LICENSE 1KB

code

obsolete

Short-Copy1.ipynb 101KB

Detection of Pneumonia from Chest X-Ray Images Modulerized.ipynb 504KB

Detection of Pneumonia from Chest X-Ray Images-Copy1.ipynb 33KB

finetuning_torchvision_models.ipynb 52KB

Detection of Pneumonia from Chest X-Ray Images 1.0.0.2.ipynb 669KB

Detection of Pneumonia from Chest X-Ray Images Final.ipynb 3.79MB

Detection of Pneumonia from Chest X-Ray Images Modulerized 4.ipynb 192KB

Detection of Pneumonia from Chest X-Ray Images 1.0.0.1.ipynb 747KB

util_helper.ipynb 3KB

Detection of Pneumonia from Chest X-Ray Images - Copy.ipynb 1.95MB

plot_precision_recall.ipynb 122KB

Detection of Pneumonia from Chest X-Ray Images.ipynb 34KB

Short2-Copy1.ipynb 28KB

Short_new.ipynb 118KB

Short.ipynb 102KB

.ipynb_checkpoints

transfer_learning_tutorial-checkpoint.ipynb 15KB

transfer_learning_tutorial.ipynb 15KB

Detection of Pneumonia from Chest X-Ray Images2.ipynb 150KB

Detection of Pneumonia from Chest X-Ray Images 1.0.0.3.ipynb 2.54MB

requirements.txt 348B

setup.py 0B

demo

images

result.png 674KB

obsolete

result.png 1.31MB

sample.png 200KB

sample

sample.png 168KB

report

CM.png 29KB

README.md 4KB

## Pneumonia Detection from Chest X-Ray Images using Transfer Learning <pre> Domain : Computer Vision, Machine Learning Sub-Domain : Deep Learning, Image Recognition Techniques : Deep Convolutional Neural Network, ImageNet, Inception Application : Image Recognition, Image Classification, Medical Imaging </pre> ### Description <pre> 1. Detected Pneumonia from Chest X-Ray images using Custom Deep Convololutional Neural Network and by retraining pretrained model “InceptionV3” with 5856 images of X-ray (1.15GB). 2. For retraining removed output layers, freezed first few layers and fine-tuned model for two new label classes (Pneumonia and Normal). 3. With Custom Deep Convololutional Neural Network attained testing accuracy 89.53% and loss 0.41. </pre> #### Code <pre> GitHub Link : <a href=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images>Detection of Pneumonia from Chest X-Ray Images(GitHub)</a> GitLab Link : <a href=https://gitlab.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images>Detection of Pneumonia from Chest X-Ray Images(GitLab)</a> Portfolio : <a href=https://anjanatiha.wixsite.com/website>Anjana Tiha's Portfolio</a> </pre> #### Dataset <pre> Dataset Name : Chest X-Ray Images (Pneumonia) Dataset Link : <a href=https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia>Chest X-Ray Images (Pneumonia) Dataset (Kaggle)</a> : <a href=https://data.mendeley.com/datasets/rscbjbr9sj/2>Chest X-Ray Images (Pneumonia) Dataset (Original Dataset)</a> Original Paper : <a href=https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5>Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning</a> (Daniel S. Kermany, Michael Goldbaum, Wenjia Cai, M. Anthony Lewis, Huimin Xia, Kang Zhang) https://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 </pre> <pre> Dataset Details Dataset Name : Chest X-Ray Images (Pneumonia) Number of Class : 2 Number/Size of Images : Total : 5856 (1.15 Gigabyte (GB)) Training : 5216 (1.07 Gigabyte (GB)) Validation : 320 (42.8 Megabyte (MB)) Testing : 320 (35.4 Megabyte (MB)) Model Parameters Machine Learning Library: Keras Base Model : InceptionV3 && Custom Deep Convolutional Neural Network Optimizers : Adam Loss Function : categorical_crossentropy For Custom Deep Convolutional Neural Network : Training Parameters Batch Size : 64 Number of Epochs : 30 Training Time : 2 Hours Output (Prediction/ Recognition / Classification Metrics) Testing Accuracy (F-1) Score : 89.53% Loss : 0.41 Precision : 88.37% Recall (Pneumonia) : 95.48% (For positive class)  </pre> ##### Sample Output: <kbd> <img src=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images/blob/master/demo/sample/sample.png> </kbd> <kbd> <a href=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images/blob/master/demo/images/result.png>See More Images</a> </kbd> ##### Confusion Matrix: <kbd> <img src=https://github.com/anjanatiha/Detection-of-Pneumonia-from-Chest-X-Ray-Images/blob/master/demo/report/CM.png alt="Confusion Matrix" width=800px height=600px> </kbd> #### Tools / Libraries <pre> Languages : Python Tools/IDE : Anaconda Libraries : Keras, TensorFlow, Inception, ImageNet </pre> #### Dates <pre> Duration : October 2018 - Current Current Version : v1.0.0.3 Last Update : 12.16.2018 </pre>