Artificial Intelligence Techniques for Classification of Eye Tumors: A Survey

Tumors that have migrated to other regions of the body, particularly the breast, lung, bowel, or prostate, usually develop secondary tumors in the eyes. Retinoblastoma in children and melanoma in adults are two forms of primary cancers that develop within the eye. In this paper, we review the recent works of the artificial intelligence techniques that are applied for classification of ophthalmology tumors. The researchers had proposed different diagnosis systems of eye cancer; iris tumor, iris nevus, uveal melanoma and metastatic, malignant choroidal melanoma and retinoblastoma. The techniques used in these papers can be divided into three main methodologies. The main methodology depends on the Artificial Neural Network (ANN) and deep learning; Back Propagation Neural Networks (BPNN), Radial Basis Function Networks (RBFN), Auto Encoder (AE) Neural Network, hybrid Stacked Auto Encoder (SAE) Network, Deep Belief Network (DBN),Convolutional Neural Network (CNN) and Extreme Learning Machine (ELM). The second methodology depends on the Machine Learning (ML) approaches; decision tree, Fuzzy C-Means (FCM), Alternative Fuzzy C-Mean (AFCM), Support Vector Machine (SVM) and Decision Tree classifier. The third one depends on different image processing techniques and Apriori based algorithm. The highest recognition rate is achieved by applying different image processing techniques and BPNN with 98.5% and 95%, respectively.