Brain tumors are extremely dangerous and can lead to a very short life expectancy at their highest grade. Identifying brain tumors early is crucial for improving survival rates, but it poses a significant challenge for medical professionals. Noise and other environmental disturbances are more likely to appear in MRI. As a result, it is challenging for doctors to diagnose the tumor and determine its causes. Therefore, a system was proposed to detect brain tumors from images. In this procedure, the image is first converted into a grayscale then filters are applied to remove environmental interference and other noise from the image. The user has to select the image. The system processes images through various image-processing steps. However, in the early stages of a brain tumor, the edges in the images are not well-defined. To address this, a deep learning model incorporating CNN and FCNN has been developed to enhance the classification of tumor types and the associated preprocessing stages. This pre-trained network is capable of categorizing images into 1000 object categories. The fully connected layers are replaced with dense connections and a softmax activation function, enabling the classification of brain tumor images into four classes.