An Employment of Probabilistic Neural Network in Magnetic Resonance Imaging For Early Brain Cancer Detection (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
Given the circumstances of the countries in which wars, political instability, and other uncertainties are passing that make the atmosphere impure, which have caused many diseases, one of these diseases that has spread widely is cancer. Cancer is a very common disease, and many of them affect a person and lead him or her to death. Among these diseases, which have been common in recent years specifically the brain tumors that they need early diagnosis and do not cause the death of the person. Furthermore, many studies in the field of brain cancer detection have been done, but the best solution is still missing. Therefore, in this paper, a reliable method is proposed to detect brain tumors, extract its properties, and classify the tumor using Magnetic Resonance Imaging (MRI) through the artificial neural network.  In the proposed system, an essential part of image processing is the analysis and processing of digital images, especially to improve their quality, Bilateral Filter is used to improving image clarity and any image noise in this method preserves edges. After that, the distinctive properties of the image are extracted using the Histogram of Oriented Gradient (HOG) method. Thus, the extracted features are strong and can be classified as a Probabilistic Neural Network (PNN), this is what distinguishes our work from the previous works. The advantage obtained is granted to the PNN Classifier, which is used to train and test the accuracy of performance in perceiving the location of the tumour in MRI images of the brain accuracy as it resolves 99.5%.