An Effective Classification of Brain Tumor using Deep Learning Technique - Balamurugan, Dr. A.; Devi, Dr. S. Siamala; G, Rajakumar; S, Sreegandh; S, Vivek (International journal of innovative technology and exploring engineering, 2020)
Health experts have increased taking advantage of the benefits of most modern technologies, thus generating a scalable improvement in the health care area. Because of this, there is a paradigm shift from manual monitoring towards more accurate virtual monitoring with minimum percentage of error. Advances in artificial intelligence (AI) led to exciting solutions with high accuracy for medical imaging technology and is a key method for enhancing future applications. Detection task of brain tumor is difficult in the medical field. Detection of brain tumors manually is time consuming and requires large number of MRI images for cancer diagnosis. So, there is a need for automatic brain tumors detection from Brain MR Images. Deep learning methods can achieve this task. Different deep learning networks can be used for the detection of brain tumors. The proposed method comprises a classification network which classifies the input MR images into 2 classes: one with tumor and the second without tumor. In this work, detection of brain tumor is done via classification by retraining the classifier using the technique known as transfer learning. The obtained result shows that our method works better than the existing methods. The most purpose of this project was to create a deep learning model that will classify if a subject features a growth or not based on MRI scan. I used the VGG-16, Inception v3, and Resnet.