Author : Gaurav Ramtri 1
Date of Publication :9th June 2020
Abstract: With the recent boost in the technology in the 21st century, a tremendous amount of data is flushing into the market, AI and Big Data have proved to be prolific for any industry adopting these methods. The use of this data has helped enterprises analyze the trends, recommend various changes in the current approaches, and introduce new techniques for the prosperity of that industry. The ongoing research in the Healthcare sector has been a hub of attraction for various Data Scientists and doctors around the globe. With the available amount of data, it is believed that soon all the medical procedures will be performed by machines. The only way for it to be possible is to make the machine work like a human brain. With the introduction of deep learning, there have been various artificial neural networks such as ResNet50, VGG16, XceptionNet, InceptionNet, etc. The main aim of these deep learning architectures is to achieve the accuracy with which the human brain functions. The human brain is known to be the most complex organ because it performs various functions such as controlling thoughts, performing actions, etc. and making a machine perform all these functions with similar accuracy as the human brain has been a challenging task. This paper aims at predicting whether a person has a brain tumor or not from the dataset containing MRI images of the brain using ResNet50 and VGG16 and comparing their performance.
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