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Brain tumor detection and multi-classification using advanced deep learning techniques
Microscopy Research and Technique ( IF 2.5 ) Pub Date : 2021-01-05 , DOI: 10.1002/jemt.23688
Tariq Sadad 1 , Amjad Rehman 2 , Asim Munir 3 , Tanzila Saba 2 , Usman Tariq 4 , Noor Ayesha 5 , Rashid Abbasi 6
Affiliation  

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.

中文翻译:

使用先进的深度学习技术进行脑肿瘤检测和多分类

如果未在早期检测到,脑肿瘤是脑癌中脑细胞不受控制的发展。早期脑肿瘤诊断对治疗计划和患者生存率起着至关重要的作用。脑肿瘤有不同的形式、特性和治疗方法。因此,手动脑肿瘤检测复杂、耗时且容易出错。因此,目前需要高精度的自动计算机辅助诊断。本文介绍了使用 ResNet50 作为 Figshare 数据集上的主干的 Unet 架构进行的分割,并实现了 0.9504 的联合交集 (IoU) 水平。引入了预处理和数据增强概念以提高分类率。脑肿瘤的多分类是使用进化算法和通过迁移学习的强化学习来执行的。其他深度学习方法如 ResNet50、DenseNet201、MobileNet V2 和 InceptionV3 也被应用。由此获得的结果表明,所提出的研究框架比现有技术报告的表现更好。不同的CNN,应用于肿瘤分类的模型,如MobileNet V2、Inception V3、ResNet50、DenseNet201、NASNet,准确率分别达到91.8、92.8、92.9、93.1、99.6%。然而,NASNet 表现出最高的准确率。不同的CNN,应用于肿瘤分类的模型,如MobileNet V2、Inception V3、ResNet50、DenseNet201、NASNet,准确率分别达到91.8、92.8、92.9、93.1、99.6%。然而,NASNet 表现出最高的准确率。不同的CNN,应用于肿瘤分类的模型,如MobileNet V2、Inception V3、ResNet50、DenseNet201、NASNet,准确率分别达到91.8、92.8、92.9、93.1、99.6%。然而,NASNet 表现出最高的准确率。
更新日期:2021-01-05
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