A Deep Learning Based Image Processing Technique for Early Lung Cancer Prediction

Nowshin Tasnim, Kazi Rifah Noor, Mursalina Islam, Mohammad Nurul Huda, Iqbal H. Sarker
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Abstract

Lung cancer is the primary cause of cancer mor-tality all over the world due to the increase of tobacco consumption, and industrialization in developing nations. As the early-stage diagnosis can reduce the mortality rate significantly, early detection with the availability of high-tech Medical facilities is highly necessary. In this research, we used deep learning (DL) methods initially on patient's 1190 CT scan images from the Kaggle IQ-OTH lung cancer dataset, and after significant image preprocessing steps we found augmented images including normal, malignant, and benign cases to identify high-risk in-dividuals to detect lung cancer and also predict the malignancy and thus, taking early actions to prevent long-term consequences. A thorough performance comparison between several classifiers, including the conventional CNN, Resnet50, and InceptionV3, has been presented. Here, affine transformation, gaussian noise, and other rigorous image preprocessing techniques are used. The contribution obtained a 98% validation accuracy while reducing the model's complexity with the previous preprocessing stage. The comparison method shows that the suggested preprocessing method yields a higher F1 score value of 97%, validating our suggested methodology.
基于深度学习的早期肺癌预测图像处理技术
由于烟草消费的增加和发展中国家的工业化,肺癌是全世界癌症发病率的主要原因。由于早期诊断可以大大降低死亡率,因此利用高科技医疗设施进行早期检测是非常必要的。在这项研究中,我们首先在 Kaggle IQ-OTH 肺癌数据集中的 1190 张患者 CT 扫描图像上使用了深度学习(DL)方法,经过重要的图像预处理步骤后,我们发现了包括正常、恶性和良性病例在内的增强图像,以识别高危人群,检测肺癌,同时预测恶性程度,从而及早采取行动,避免长期后果。本文对几种分类器(包括传统 CNN、Resnet50 和 InceptionV3)进行了全面的性能比较。这里使用了仿射变换、高斯噪声和其他严格的图像预处理技术。该贡献获得了 98% 的验证准确率,同时降低了模型在前一预处理阶段的复杂度。对比方法显示,建议的预处理方法产生了更高的 F1 分数,达到 97%,验证了我们建议的方法。
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