Cancer Detection from Microscopic-Tissue Images with Deep Learning (Auto ML, Custom Convolutional Neural Network, and Transfer Learning)
Domain : Computer Vision, Machine Learning
Sub-Domain : Deep Learning, Image Recognition
Techniques : Deep Convolutional Neural Network, Transfer Learning, ImageNet, Auto ML, NASNetMobile
Application : Image Recognition, Image Classification, Medical Imaging
Description
1. Detected Cancer from microscopic tissue images (histopathologic) with Auto ML (Google’s “NASNet”).
2. For training, concatenated global pooling (max, average), dropout and dense layers to the output layer for final output prediction.
3. Attained testing accuracy of 93.72% and loss 0.30 on 250K+ (6.5GB+) image cancer dataset.
Code
GitHub Link : Histopathologic Cancer Detection(GitHub)
GitLab Link : Histopathologic Cancer Detection(GitLab)
Portfolio : Anjana Tiha's Portfolio
Dataset
Dataset Name : Histopathologic Cancer Detection
Dataset Link : Histopathologic Cancer Detection (Kaggle)
: PatchCamelyon (PCam) (GitHub)
: CAMELYON16 challenge Dataset (Original Dataset)
Original Paper : Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer
Authors: Babak Ehteshami Bejnordi, Mitko Veta, Paul Johannes van Diest
JAMA (The Journal of the American Medical Association)
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, et al. Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer. JAMA. 2017;318(22):2199–2210. doi:10.1001/jama.2017.14585
Dataset Details
Dataset Name : Histopathologic Cancer Detection
Number of Class : 2
Dataset Subtype |
Number of Image |
Size of Images (GB/Gigabyte) |
Total |
220,025 |
5.72 GB |
Training |
132,016 |
3.43 GB |
Validation |
44,005 |
1.14 GB |
Testing |
44,004 |
1.14 GB |
Model and Training Prameters
Current Parameters |
Value |
Base Model |
NashNetLarge |
Optimizers |
Adam |
Loss Function |
Categorical Crossentropy |
Learning Rate |
0.0001 |
Batch Size |
16 |
Number of Epochs |
2 |
Training Time |
4.5 hour (270 min) |
Model Performance Metrics (Prediction/ Recognition / Classification)
Dataset |
Training |
Validation |
Test |
Accuracy |
94.74% |
93.62% |
93.72% |
Loss |
0.14 |
0.30 |
0.30 |
Precision |
--- |
--- |
89.02% |
Recall |
--- |
--- |
90.80% |
Roc-Auc |
--- |
--- |
91.59% |
Other Experimented Model and Training Prameters
Parameters (Experimented) |
Value |
Base Models |
NashNet(NashNetLarge, NashNetMobile), InceptionV3 |
Optimizers |
Adam, SGD |
Loss Function |
Categorical Crossentropy, Binary Crossentropy |
Learning Rate |
0.0001, 0.00001, 0.000001, 0.0000001 |
Batch Size |
16, 32, 64, 128, 256 |
Number of Epochs |
2, 4, 6, 10, 30, 50, 100 |
Training Time |
4.5 hour (270 min), 1 day (24 hours), 2 days (24 hours) |
Sample Output:
See More Images
Confusion Matrix:
Tools / Libraries
Languages : Python
Tools/IDE : Anaconda
Libraries : Keras, TensorFlow, Inception, ImageNet
Dates
Duration : November 2018 - March 2019
Current Version : v1.0.0.8
Last Update : 03.24.2019