|Appears in Collections:||Computing Science and Mathematics Conference Papers and Proceedings|
|Title:||Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver|
|Citation:||Ali L, Hussain A, Li J, Shah A, Sudhakr U, Mahmud M, Zakir U, Yan X, Luo B & Rajak M (2014) Intelligent image processing techniques for cancer progression detection, recognition and prediction in the human liver. In: 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE). 2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE), Orlando, FL, USA, 09.12.2014-12.12.2014. Piscataway, NJ, USA: IEEE. https://doi.org/10.1109/cicare.2014.7007830|
|Conference Name:||2014 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)|
|Conference Dates:||2014-12-09 - 2014-12-12|
|Conference Location:||Orlando, FL, USA|
|Abstract:||Clinical Decision Support (CDS) aids in early diagnosis of liver cancer, a potentially fatal disease prevalent in both developed and developing countries. Our research aims to develop a robust and intelligent clinical decision support framework for disease management of cancer based on legacy Ultrasound (US) image data collected during various stages of liver cancer. The proposed intelligent CDS framework will automate real-time image enhancement, segmentation, disease classification and progression in order to enable efficient diagnosis of cancer patients at early stages. The CDS framework is inspired by the human interpretation of US images from the image acquisition stage to cancer progression prediction. Specifically, the proposed framework is composed of a number of stages where images are first acquired from an imaging source and pre-processed before running through an image enhancement algorithm. The detection of cancer and its segmentation is considered as the second stage in which different image segmentation techniques are utilized to partition and extract objects from the enhanced image. The third stage involves disease classification of segmented objects, in which the meanings of an investigated object are matched with the disease dictionary defined by physicians and radiologists. In the final stage; cancer progression, an array of US images is used to evaluate and predict the future stages of the disease. For experiment purposes, we applied the framework and classifiers to liver cancer dataset for 200 patients. Class distributions are 120 benign and 80 malignant in this dataset.|
|Status:||VoR - Version of Record|
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