Team 28

We will implement a non-existing system with a high accuracy, speed differentiation and detection of various type of cancers . Our cancer detector goes through three essential stages. Firstly, uploading image for skin. Secondly, testing the image after applying different algorithms. Finally, classifying whether the disease is (melomna, basic cell cariona, nevus ).

Starting with pre-processing which will be performed on the uploaded image from mobile phone that will be made on the mobile . Then, segmentation by applying threshold which will be made on the server . After that is feature extraction, mainly extracting 11 features which are (Mean , Standard Deviation ,Skewness ,Kurtosis ,Smoothness, Homogenity , Contrast , Energy,Entropy,surf,sift). Final step is classification using different algorithms to train and test the dataset.

The system stores medical records to save patients medical history And the system have a doctor sign up that is made by the admin and the patient can register easily . It also has the ability of learning from miss-classified tests to enhance the future accuracy of the system.