An optimized convolutional neural network architecture for lung cancer detection.

IF 6.6 3区 医学 Q1 ENGINEERING, BIOMEDICAL APL Bioengineering Pub Date : 2024-06-11 eCollection Date: 2024-06-01 DOI:10.1063/5.0208520
Sameena Pathan, Tanweer Ali, Sudheesh P G, Vasanth Kumar P, Divya Rao
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Abstract

Lung cancer, the treacherous malignancy affecting the respiratory system of a human body, has a devastating impact on the health and well-being of an individual. Due to the lack of automated and noninvasive diagnostic tools, healthcare professionals look forward toward biopsy as a gold standard for diagnosis. However, biopsy could be traumatizing and expensive process. Additionally, the limited availability of dataset and inaccuracy in diagnosis is a major drawback experienced by researchers. The objective of the proposed research is to develop an automated diagnostic tool for screening of lung cancer using optimized hyperparameters such that convolutional neural network (CNN) model generalizes well for universally obtained computerized tomography (CT) slices of lung pathologies. The aforementioned objective is achieved in the following ways: (i) Initially, a preprocessing methodology specific to lung CT scans is formulated to avoid the loss of information due to random image smoothing, and (ii) a sine cosine algorithm optimization algorithm (SCA) is integrated in the CNN model, to optimally select the tuning parameters of CNN. The error rate is used as an objective function, and the SCA algorithm tries to minimize. The proposed method successfully achieved an average classification accuracy of 99% in classification of lung scans in normal, benign, and malignant classes. Further, the generalization ability of the proposed model is tested on unseen dataset, thereby achieving promising results. The quantitative results prove the efficacy of the system to be used by radiologists in a clinical scenario.

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用于肺癌检测的优化卷积神经网络架构
肺癌是影响人体呼吸系统的凶险恶性肿瘤,对个人的健康和福祉具有毁灭性影响。由于缺乏自动化和无创的诊断工具,医护人员将活检作为诊断的黄金标准。然而,活组织检查可能会造成创伤,而且费用昂贵。此外,数据集的有限性和诊断的不准确性也是研究人员遇到的一个主要问题。拟议研究的目标是利用优化的超参数开发一种筛查肺癌的自动诊断工具,使卷积神经网络(CNN)模型能够很好地泛化普遍获得的肺部病理计算机断层扫描(CT)切片。上述目标是通过以下方式实现的:(i) 首先,制定了专门针对肺部 CT 扫描的预处理方法,以避免随机图像平滑造成的信息损失;(ii) 在 CNN 模型中集成了正弦余弦算法优化算法(SCA),以优化选择 CNN 的调整参数。误差率被用作目标函数,SCA 算法试图将其最小化。所提出的方法在肺部扫描正常、良性和恶性分类中的平均分类准确率达到了 99%。此外,还在未见数据集上测试了所提模型的泛化能力,从而取得了令人满意的结果。定量结果证明了放射科医生在临床场景中使用该系统的有效性。
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来源期刊
APL Bioengineering
APL Bioengineering ENGINEERING, BIOMEDICAL-
CiteScore
9.30
自引率
6.70%
发文量
39
审稿时长
19 weeks
期刊介绍: APL Bioengineering is devoted to research at the intersection of biology, physics, and engineering. The journal publishes high-impact manuscripts specific to the understanding and advancement of physics and engineering of biological systems. APL Bioengineering is the new home for the bioengineering and biomedical research communities. APL Bioengineering publishes original research articles, reviews, and perspectives. Topical coverage includes: -Biofabrication and Bioprinting -Biomedical Materials, Sensors, and Imaging -Engineered Living Systems -Cell and Tissue Engineering -Regenerative Medicine -Molecular, Cell, and Tissue Biomechanics -Systems Biology and Computational Biology
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