The following information was submitted:
Transactions: INTERNATIONAL JOURNAL of COMPUTERS
Transactions ID Number: 20-251
Full Name: Zakaria Zubi
Position: Associate Professor
Age: ON
Sex: Male
Address: Sirte University, Faculty of Science, Computer Science Department Sirte, P.O Box 727, Libya,
Country: LIBYA
Tel: +218913752962
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E-mail address: zszubi@yahoo.com
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Title of the Paper: Implementing Data Mining Methods for Diagnosing Lung Cancer Patients
Authors as they appear in the Paper: Zakaria Suliman Zubi
Email addresses of all the authors: zszubi@yahoo.com
Number of paper pages: 12
Abstract: Abstract: - Lung cancer is a disease of uncontrolled cell growth in tissues of the lung, the huge majority of primary lung cancers are carcinomas of the lung, derivative from epithelial cells. Lung cancer, the most common cause of cancer-related death in men and women. Medical images' mining involves many processes. Medical images mining is a promising area of computational intelligence applied to automatically analyze patients' records aiming at the discovery of new knowledge potentially useful for medical decision making. The methods in this proposal classify the digital X-ray images in two categories: normal and abnormal. The normal ones are those characterizing a healthy patient. The abnormal ones include Types of lung cancer; we will use a common classification method, namely neural networks, but significantly improve the accuracy rate of the classifier compared to other published results using the same data set. In addition, we investigate the use of associat!
ion rules in the problem of x-ray image categorization and demonstrate with encouraging results that association rule mining is a promising alternative in medical image classification and certainly deserves more attention.
Keywords: Key-Words: - Data Mining, Classification, Medical Imaging, Image Recognition, Neural Networks, Association Rule Mining, Early Cancer Diagnosing.
EXTENSION of the file: .doc
Special (Invited) Session: Using Some Data Mining Techniques for Early Diagnosis of Lung Cancer
Organizer of the Session: 650-123
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