Presenter: Muhammad Atif Tahir, National University of Computer and Emerging Sciences, Karachi Campus, Pakistan
Paper: http://ceur-ws.org/Vol-1984/Mediaeval_2017_paper_44.pdf
Video: https://youtu.be/bumlycRIDlQ
Authors: Syed Sadiq Ali Naqvi, Shees Nadeem, Muhammad Zaid, Muhammad Atif Tahir
Abstract: An endoscopy is a procedure in which a doctor uses specialized instruments to view and operate on the internal organs and vessels of the body. This paper aims to predict the diseases and abnormalities in the Gastro-Intestinal Tract, using multimedia data. It differs from other projects in the medical domain because it does not use medical imaging like X-rays, CT scan etc. The dataset, which comprises of 4000 images, is provided by MediaEval Benchmarking Initiative for Multimedia Evaluation. The data is collected during traditional colonoscopy procedures. Techniques from the fields of multimedia content analysis (to extract information from the visual data) and machine learning (for classification) have been used. On testing data, 94% accuracy and an MCC of 0.73 is achieved using logistic regression and ensemble on different features.
Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b
MediaEval 2017 - Medical Multimedia Task: Ensemble of Texture Features for finding abnormalities in the Gastro-Intestinal Tract
1. FAST NU Data Science Group
Presented By: Dr Atif Tahir
2. Extraction of Discriminating Features Using
classical Image Processing Techniques
Selection of the Best Subset of Features
using Cross Validation
Apply Machine Learning Algorithms
to achieve sound predictions
3.
4. We try to come up with the features which
can plot a line of discrimination between the
specified classes
To calculate Texture and other important
descriptions from an image , we extract Local
Binary Patterns (LBP) , Local Ternary Patterns
(LTP) and Haralick Texture Features
5. We train separate Logistic Regression Model
on each of the features including the Pre-
computed and self extracted ones
Ensemble of all the models was then used for
the prediction
We found the best results on all metric
evaluation systems when the model is trained
on all 4000 Training examples or on 2000
examples
6. Run1: Logistic Regression on all training data
Run2: Logistic Regression on half training data
Run3: Log. Reg. on previous features as well as a
new feature added from KDA
Run4: Nearest Neighbour on KDA feature
Run5: Same as Run1 using 732 images (Selected
by KMeans Clustering)
7.
8.
9.
10. We basically opted for “one vs rest” strategy
while training the Logistic regression
classifier
For 8 classes , we fit 8 independent Binary
Classifiers (one for each class)
Prediction is then based on maximal voting
11. We present our proposed model to classify
gastro-intestinal abnormalities using
endoscopic images
The best of the results were found on logistic
regression using ensemble method on 6
different features (including Local Binary
Pattern, Haralick texture feature)
