Comparative analysis on deep convolutional neural network for brain tumor data set

R. Vinayaga Moorthy; A. Gopi Kannan; R.  Balasubramanian

doi:10.53730/ijhs.v6nS1.4949

Authors

R. Vinayaga Moorthy
vinayagamoorthy3@gmail.com
Research scholar, Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
A. Gopi Kannan Research scholar, Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
R. Balasubramanian Professor, Department of Computer Science & Engineering, Manonmaniam Sundaranar University, Tirunelveli, India

Keywords:

deep learning, CNN, LeNet, AlexNet, VGG Net-16, ResNet-18

Abstract

Deep Learning is a subdivision of machine learning and Artificial intelligence (AI). Autonomous Deep learning enables human brain to think and learn computers. In recent days Deep learning is used in many domains, especially in medical field. It is used mainly in classification. The Convolutional Neural Network (CNN) is one among the best technique in DL. It is best suitable in image classifications. CNN is directed to process the data into multiple layers of arrays. It is used for computationally efficient. Brain Tumor is one of the dangerous diseases in India as well as the whole world. A brain tumor is an unwanted cell in the brain. Brain tumor symptoms are based on size, location and type .There are two types of brain tumor. Brain tumor tissue affects on the brain that is called primary tumor. Brain tumor tissue affects in outside the brain that is called as secondary tumor (metastatic).In this paper, we are analyzing various Deep Convolution Neural Network on brain tumor perspectives. Here, LeNet, AlexNet, ResNet-18, VGG Net-16 are discussed and Evaluation metrics like Accuracy, F1 score, Precision, Recall are used to identify the performance of the above techniques.

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