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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:
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