Efficient simultaneous segmentation and classification of brain tumors from MRI scans using deep learning

IF 5.3 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biocybernetics and Biomedical Engineering Pub Date : 2023-07-01 DOI:10.1016/j.bbe.2023.08.003
Akshya Kumar Sahoo , Priyadarsan Parida , K. Muralibabu , Sonali Dash
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引用次数: 1

Abstract

Brain tumors can be difficult to diagnose, as they may have similar radiographic characteristics, and a thorough examination may take a considerable amount of time. To address these challenges, we propose an intelligent system for the automatic extraction and identification of brain tumors from 2D CE MRI images. Our approach comprises two stages. In the first stage, we use an encoder-decoder based U-net with residual network as the backbone to detect different types of brain tumors, including glioma, meningioma, and pituitary tumors. Our method achieved an accuracy of 99.60%, a sensitivity of 90.20%, a specificity of 99.80%, a dice similarity coefficient of 90.11%, and a precision of 90.50% for tumor extraction. In the second stage, we employ a YOLO2 (you only look once) based transfer learning approach to classify the extracted tumors, achieving a classification accuracy of 97%. Our proposed approach outperforms state-of-the-art methods found in the literature. The results demonstrate the potential of our method to aid in the diagnosis and treatment of brain tumors.

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利用深度学习对MRI扫描中的脑肿瘤进行有效的同时分割和分类
脑肿瘤很难诊断,因为它们可能具有相似的放射学特征,彻底的检查可能需要相当长的时间。为了解决这些挑战,我们提出了一种从二维CE MRI图像中自动提取和识别脑肿瘤的智能系统。我们的方法包括两个阶段。在第一阶段,我们使用基于编码器-解码器的U-net,以残馀网络为骨干来检测不同类型的脑肿瘤,包括胶质瘤、脑膜瘤和垂体瘤。该方法的肿瘤提取准确率为99.60%,灵敏度为90.20%,特异性为99.80%,骰子相似系数为90.11%,精密度为90.50%。在第二阶段,我们采用基于YOLO2(你只看一次)的迁移学习方法对提取的肿瘤进行分类,分类准确率达到97%。我们提出的方法优于文献中发现的最先进的方法。结果证明了我们的方法在脑肿瘤诊断和治疗方面的潜力。
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来源期刊
CiteScore
16.50
自引率
6.20%
发文量
77
审稿时长
38 days
期刊介绍: Biocybernetics and Biomedical Engineering is a quarterly journal, founded in 1981, devoted to publishing the results of original, innovative and creative research investigations in the field of Biocybernetics and biomedical engineering, which bridges mathematical, physical, chemical and engineering methods and technology to analyse physiological processes in living organisms as well as to develop methods, devices and systems used in biology and medicine, mainly in medical diagnosis, monitoring systems and therapy. The Journal''s mission is to advance scientific discovery into new or improved standards of care, and promotion a wide-ranging exchange between science and its application to humans.
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