A Transfer Learning approach for AI-based classification of brain tumors
A Transfer Learning approach for AI-based classification of brain tumors
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DOI：
10.1016/j.mlwa.2020.100003
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发表时间：
2020-12-15
影响因子：
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通讯作者：
Anand, R. S.
中科院分区：
文献类型：
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作者：
Mehrotra, Rajat;Ansari, M. A.;Anand, R. S.
脑肿瘤的分类是评估肿瘤和制定适当治疗方案的重要任务。存在用于识别脑中的肿瘤的许多成像模态。磁共振成像（MRI）通常用于这样的任务，因为其无与伦比的图像质量和它不依赖于电离辐射的现实。人工智能（AI）以深度学习（DL）的形式在医学成像领域的相关性为分类和检测复杂病理条件（如脑肿瘤等）的非凡发展铺平了道路。在这项工作中，提出了使用深度学习算法的基于AI的BT分类，用于利用开放访问的数据集对脑肿瘤的类型进行分类。这些数据集将BT分类为（恶性和良性）。该数据集包括696张T1加权图像，用于测试目的。预计的安排完成了一个值得注意的性能与最好的准确度为99.04%。所取得的成果表明所提出的算法用于脑肿瘤分类的能力。
Classification of Brain Tumor (BT) is a vital assignment for assessing Tumors and making a suitable treatment. There exist numerous imaging modalities that are utilized to identify tumors in the brain. Magnetic Resonance Imaging (MRI) is generally utilized for such a task because of its unrivaled quality of the image and the reality that it does not depend on ionizing radiations. The relevance of Artificial Intelligence (AI) in the form of Deep Learning (DL) in the area of medical imaging has paved the path to extraordinary developments in categorizing and detecting intricate pathological conditions, like a brain tumor, etc. Deep learning has demonstrated an astounding presentation, particularly in segmenting and classifying brain tumors. In this work, the AI -based classification of BT using Deep Learning Algorithms are proposed for the classifying types of brain tumors utilizing openly accessible datasets. These datasets classify BTs into (malignant and benign). The datasets comprise 696 images on T1 -weighted images for testing purposes. The projected arrangement accomplishes a noteworthy performance with the finest accuracy of 99.04%. The achieved outcome signifies the capacity of the proposed algorithm for the classification of brain tumors.