earticle

영어

Among the major reasons for death in humans, brain tumors are the most prevalent type and it affects humans of all ages. Brain tumors are treatable if detected in early stages. The classification of Tumors is being done by biopsy. On the Other hand, Magnetic Resonance Imaging (MRI) is a routine technique for humans to investigate this disease (Brain Tumors). In contrast, avoiding the need for a Radiologist, the detection and classification method proposed by using the Deep Learning Technique in this paper would benefit to all doctors globally. This work focused on a new Sequential base Convolutional Neutral Network (CNN) Architecture to classify the Brain Tumor types such as Glioma-Tumors, Meningioma tumors, No-tumors, and Pituitary tumors using MRI images. The proposed method gives better results for classifying Brain Images from a given dataset of Brain tumors with around 3264 MRI images. The purpose of our work is to use the Sequential base CNN model to detect brain cancers. The accuracy of our model's performance will be assessed. Consequently, we may infer that the Sequential base CNN model produces results that are very adequate and have an increased accuracy. Finally, the proposed method improves the accuracy up to 82.66%.