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The automated recognition of Posterior Fossa Tumors (PFT) in Magnetic Resonance Imaging (MRI) plays a vital role, as it furnishes essential data about irregular tissue, essential for treatment planning. Human examination has traditionally been the standard approach for identifying defects in brain MRI. This technique is unsuitable for a massive quantity of data. Therefore, automated PFT detection techniques are being established to minimize radiologist's time. In this paper, the posterior fossa tumor is detected and classified in brain MRI using Convolutional Neural Network (CNN) algorithms, and the model result and accuracy obtained from each algorithm are explained. A dataset collection made up of 3,00,000 images with an average of 500 patients from the Children's Cancer Hospital Egypt (CCHE) was used. The CNN algorithms investigated to classify the PFT were VGG19, VGG16, and ResNet50. Moreover, explanations for the behavior of networks were investigated using three different techniques: LIME, SHAP, and ICE. 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