Brain tumor detection using cnn

• Research Paper • 22 April 2021 Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework • ORCID: orcid.org/0000-0002-7981-2305 Iranian Journal of Science and Technology, Transactions of Electrical Engineering volume 45, pages 1015–1036 ( 2021) Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy specimens today. The current method is invasive, time-consuming and prone to manual errors. These disadvantages show how essential it is to perform a fully automated method for multi-classification of brain tumors based on deep learning. This paper aims to make multi-classification of brain tumors for the early diagnosis purposes using convolutional neural network (CNN). Three different CNN models are proposed for three different classification tasks. Brain tumor detection is achieved with 99.33% accuracy using the first CNN model. The second CNN model can classify the brain tumor into five brain tumor types as normal, glioma, meningioma, pituitary and metastatic with an accuracy of 92.66%. The third CNN model can classify the brain tumors into three grades as Grade II, Grade III and Grade IV with an accuracy of 98.14%. All the important hyper-parameters of CNN models are automatically designated using the grid search optimization algorithm. To the best of author’s knowledge, this is the first study for multi-classification of brain tumor MRI images using CNN whose almost all hyper...

Brain tumor identification is an essential task for assessing the tumors and its classification based on the size of tumor. There are various types of imaging strategies such as X-rays, MRI, CT-scan used to recognize brain tumors. Computed Tomography (CT) scan images are used for in this work for Brain tumor Image Identification. CT-scan images are used, because as it gives size, shape and blood vessels detailing and is non-invasive technique. CT-scan is commonly utilized because of the superior quality of image. Deep learning (DL) is the most recent technology which gives higher efficiency results in recognition, classification. In this paper, the model is developed by using Convolution neural network to detect the tumor of brain image from a dataset from Kaggle. The dataset contains near about 1000 images. Tumor is identified by image processing algorithm using CNN, time complexity is 90 m sec, and the accuracy of the present system is 97.87%. Keywords • Brain tumor • CT scan images • Deep neural network • Convolution neural network • Spyder (Python 3.7) • DeAngelis LM (2001) Brain tumors. N Engl J Med 344(2):114–123 • Sultan HH, Salem NM, Al-Atabany W (2019) Multi-classification of brain tumor images using deep neural network. Special section on Deep Learning for computer Aided diagnosis. IEEE Access 7. • Stewart BW, Wild CP (2014) World cancer report 2014. IARC, Lyon, France • Brain, Other CNS and Intra cranial Tumors Statistics. Accessed: May 2019. [Online]. Available...

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Brain tumor represents one of the most fatal cancers around the world. It is common cancer in adults and children. It has the lowest survival rate and various types depending on their location, texture, and shape. The wrong classification of the tumor brain will lead to bad consequences. Consequently, identifying the correct type and grade of tumor in the early stages has an important role to choose a precise treatment plan. Examining the magnetic resonance imaging (MRI) images of the patient’s brain represents an effective technique to distinguish brain tumors. Due to the big amounts of data and the various brain tumor types, the manual technique becomes time-consuming and can lead to human errors. Therefore, an automated computer assisted diagnosis (CAD) system is required. The recent evolution in image classification techniques has shown great progress especially the deep convolution neural networks (CNNs) which have succeeded in this area. In this regard, we exploited CNN for the problem of brain tumor classification. We suggested a new model, which contains various layers in the aim to classify MRI brain tumor. The proposed model is experimentally evaluated on three datasets. Experimental results affirm that the suggested approach provides a convincing performance compared to existing methods. • Razzak MI, Imran M, Xu G (2018) Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inform 23(5):1911–1919 ...