Cancerous tumors considered being one of the acute diseasesthat cause the human death especially brain cancers.Many computer-aided diagnosis systems are now widelyspread to aid in brain tumors diagnosis. Therefore, anautomated and reliable computer-aided diagnostic system fordiagnosing and classifying the brain tumor has been proposed[1].MRI (Magnetic resonance Imaging) is one source of braintumors detection tools, but using MRI in children braintumors classification is considered to be difficult processaccording to the variance and complexity of tumors. Thispaper presents a survey of the most famous techniques usedfor the classification of brain tumors based on children MRI[2].The brain tumors detection and classification systems consistof four stages, namely, MRI preprocessing, Segmentation,Feature extraction, and Classification stages respectively. Inthe first stage, the main task is to eliminate the medicalresonance images (MRI) noise which may cause due to lightreflections or any inaccuracies in the imaging medium.The second stage, which is the stage where the region ofinterest is extracted (tumor region). In the third stage, thefeatures related to MRI images will be obtained and stored inan image vector to be ready for the classification process. Andfinally the fourth stages, where classifier will take place tospecify the tumor kinds.TANNN is a new classification technique user to get a veryhigh performance compared with other classificationtechniques such as KNN, SVM, DT, and Naïve Bayes.Image classification is an important task in the imageprocessing and especially in the medical diagnosis field.Image classification refers to the process of labeling imagesinto one of a number of predefined categories. In this survey,the test of various classification techniques against each otherwill be present.
(20) (PDF) Real Brain Tumors Datasets Classification using TANNN. Available from: https://www.researchgate.net/publication/305361947_Real_Brain_Tumors_Datasets_Classification_using_TANNN [accessed Apr 02 2023]. |
