| Classifying white blood cells using data mining algorithms to enhance the healthcare system |
| Paper ID : 1095-ISCBAS |
| Authors |
|
Humam Khalid Yaseen *1, Kamal El-Dahshan2, Mohammad Taha Abou-Kreisha3, khaled Ahmed Fathy3 1Iraqi Sunni Affairs 2Professor in Computer science 3Alazhar university |
| Abstract |
| The detection and classification of each type of White Blood Cell (WBC) count are crucial for human healthcare since some human diseases can be identified by each type's WBC count. This paper approaches the problem of classifying white blood cell types. The main contribution of this work is to merge some data mining algorithms, such as deep learning and shallow machine learning algorithms, in addition to transfer learning, to make one decision. This paper takes advantage of deep learning in extract features from images and the superpower of shallow machine learning in classification. As a result, the proposed model showed good accuracy in the classification by 99.8% advanced in the literary works. |
| Keywords |
| Data mining, Healthcare system, White blood cells, Deep learning, Shallow machine learning |
| Status: Abstract Accepted (Oral Presentation) |