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| Tumor Classification Using Artificial Neural Networks |
Jamal Khamis El-Mahelawi, Jinan Usama Abu-Daqah, Rasha Ibrahim Abu-Latifa, Bassem S. Abu-Nasser, Samy S. Abu-Naser
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Abstract:
Tumor is a group of diseases that involve abnormal increases in the number of cells, with the potential to invade or spread to other parts of the body. Not all tumors or lumps are cancerous; benign tumors are not classified as being cancer because they do not spread to other parts of the body. There are over 100 different known Tumors that affect humans. Tumors are often described by the body part that they originated in. However, some body parts contain multiple types of tissue, so for greater precision, tumors are additionally classified by the type of cell that the tumor cells originated from. The aim of this study is to propose an Artificial Neural Network model for the classification of tumor types. Some of important features in the classification of the tumors are age, sex, histologic-type, degree-of-diffe, status of bone, bone-marrow, lung, pleura, peritoneum, liver, brain, skin, neck, supraclavicular, axillar, mediastinum, and abdominal. They were used as input variables for the ANN model. A model based on the Multilayer Perceptron topology was created and trained using "primary tumor" dataset which was collected from the University Medical Centre, Institute of Oncology, Ljubljana, Evaluation of the ANN model showed that the ANN model is able to correctly classify the tumor type with 79.65 % accuracy rate.
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