A Machine Learning Entrenched Brain Tumor Recognition Framework
Brain tumor; Confusion matrix; Convolutional Neural Networks; Image processing; Machine learning; Mobile application
Brain tumor detection plays a significant role in medical image processing. Treatment for patients with brain tumors is primarily dependent on faster detection of these tumors. More rapid detection of brain tumors will help in the improvement of the patient's life chances. Diagnosis of brain tumors by doctors most commonly follow manual segmentation, which is difficult and time-consuming; instead, automatic detection is necessary. Nowadays, automatic detection plays a vital role and can be a solution to detecting brain tumors with better performance. Brain tumor detection using the MRI images method is an essential diagnostic tool for predicting brain tumors; the implementation for these kinds of detection can be done using various machine learning algorithms and methodologies. It helps the doctors understand the actual progression of the evolving tumor, allowing the doctors to decide how the treatment has to be given for that particular patient and measures required to follow up. Therefore, the intention is to create a framework to detect brain tumors in MRI images using a machine learning algorithm and analyze the performance of the brain tumor detection using sensitivity and specificity, which helps us to analyze how well the algorithm has performed in detecting the brain tumors accurately and develop a mobile application framework in which the MRI images can be directly scanned to know whether the cancer is present in a scanned MRI image or not. 2022 IEEE.
Proceedings of the International Conference on Electronics and Renewable Systems, ICEARS 2022, pp. 1372-1376.
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICEARS53579.2022.9752195 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85128934305&doi=10.1109%2fICEARS53579.2022.9752195&partnerID=40&md5=ff25dcfe71bdddc3401add20e04b2c34
Surya S., Christ University, Dept. Computer Science, Bangalore, India; Aurelia S., Christ University, Dept. Computer Science, Bangalore, India