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Development of a Computer Aided System for Early Detection and Diagnosis of Breast and Lung Cancers using Advanced Mathematical Techniques
The Department of Mathematics and Statistics of the College of Arts and Sciences invites you to a seminar to be conducted by Dr. Samir Brahim Belhaouari from the Math Department of the University of Sharjah.
Lecture: Development of a Computer Aided System for Early Detection and Diagnosis of Breast and Lung Cancers using Advanced Mathematical Techniques; Extended Results for Face Detection/Gas Identification
Cancer is a disease which considered as one of the main causes of mortality worldwide. This disease has been defined by the World Health Organization (WHO) as the uncontrolled growth of abnormal (malignant) cells; and based on WHO, the lifestyle of an individual (e.g., overweight, smoking) has an impact on being affected with cancer. This disease can affect any part in the human body such as breast, lung, colon, brain, prostate, liver, etc. In 2008, the GLOBOCAN program reported that 7.6 million people died worldwide because of cancer where 70 percent of those lived in low and middle income countries (where the common types of cancer that affect the population are lung, breast, colon, liver and stomach cancers). This high percentage in such countries could be due to the low quality of health care.
Early detection/diagnosis of cancer are the most promising approaches to reduce the number of deaths since they direct medical practitioners/physicians to early treatment. In order to diagnose diseases at the early stage, computer aided diagnosis (CAD) has been a key approach in identifying abnormalities through acquiring images of human organs and analyzing them. The images acquired from the human body used to be analyzed by radiologists alone, however, due to the difficulties radiologists face in perceiving subtle regions, CAD system were introduced. As this is a significant area of research, many CAD systems have been developed for many types of cancer. However, high false positives and detecting subtle regions are still among the issues that researchers are working to tackle. CAD systems with high false positives decrease the efficiency of the diagnosis; therefore, the number of false negatives should be decreased.
For further details kindly contact Dr. Ayman Badawi email@example.com.