Name
Abstract | ||
|---|---|---|
This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can, not only generate labels (normal and abnormal) for a given ultrasound image but, it can also detect when its prediction is likely to be incorrect. The proposed model abstains from generating the label of a test example if it is not confident about its prediction. Such behavior is commonly practiced by medical doctors who, when given insufficient information or a difficult case, can choose to carry out further clinical or diagnostic tests before generating a diagnosis. However, existing machine learning models are designed in a way to always generate a label for a given example even when the confidence of their prediction is low. We have proposed a novel stochastic gradient descent based solver for the learning with abstention paradigm and use it to make a practical, state of the art method for liver disease classification. The proposed method has been benchmarked on a data set of approximately 100 patients from MINAR, Multan, Pakistan and our results show that the performance of the proposed scheme is at par with medical experts. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/FIT.2017.00070 | 2017 International Conference on Frontiers of Information Technology (FIT) |
Keywords | Field | DocType |
Ultrasound,Liver disease,learning with abstention,learning with rejection,machine learning,fatty liver disease,heterogeneous liver texture | Stochastic gradient descent,Computer science,Diagnostic test,Support vector machine,Artificial intelligence,Solver,Ultrasonic imaging,Machine learning,Ultrasound image | Journal |
Volume | ISSN | ISBN |
abs/1811.04463 | 2334-3141 | 978-1-5386-3568-1 |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kanza Hamid | 1 | 0 | 0.34 |
| Amina Asif | 2 | 5 | 5.51 |
| wajid arshad abbasi | 3 | 8 | 2.29 |
| Durre Sabih | 4 | 15 | 1.20 |
| Fayyaz ul Amir Afsar Minhas | 5 | 27 | 9.37 |