Turk Omer, Acar Emrullah, Irmak Emrah, Yilmaz Musa, Bakis Enes
Faculty of Engineering and Architecture, Department of Computer Engineering, Mardin Artuklu University, Mardin, Turkey.
Faculty of Engineering and Architecture, Department of Electrical and Electronics Engineering, Batman University, Batman, Turkey.
Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241301297. doi: 10.1177/15330338241301297.
Cancer is a significant public health issue due to its high prevalence and lethality, particularly lung and colon cancers, which account for over a quarter of all cancer cases. This study aims to enhance the detection rate of lung and colon cancer by designing an automated diagnosis system. The system focuses on early detection through image pre-processing with a 2D Gaussian filter, while maintaining simplicity to minimize computational requirements and runtime. The study employs three Convolutional Neural Network (CNN) models-MobileNet, VGG16, and ResNet50-to diagnose five types of cancer: Colon Adenocarcinoma, Benign Colonic Tissue, Lung Adenocarcinoma, Benign Lung Tissue, and Lung Squamous Cell Carcinoma. A large dataset comprising 25 000 histopathological images is utilized. Additionally, the research addresses the need for safety levels in the model by using Class Activation Mapping (CAM) for explanatory purposes. Experimental results indicate that the proposed system achieves a high diagnostic accuracy of 99.38% for lung and colon cancers. This high performance underscores the effectiveness of the automated system in detecting these types of cancer. The findings from this study support the potential for early diagnosis of lung and colon cancers, which can facilitate timely therapeutic interventions and improve patient outcomes.
由于癌症的高发病率和致死率，它是一个重大的公共卫生问题，尤其是肺癌和结肠癌，这两种癌症占所有癌症病例的四分之一以上。本研究旨在通过设计一个自动诊断系统来提高肺癌和结肠癌的检测率。该系统专注于通过使用二维高斯滤波器进行图像预处理来实现早期检测，同时保持简单性以最小化计算需求和运行时间。该研究采用三种卷积神经网络（CNN）模型——MobileNet、VGG16和ResNet50——来诊断五种癌症类型：结肠腺癌、良性结肠组织、肺腺癌、良性肺组织和肺鳞状细胞癌。使用了一个包含25000张组织病理学图像的大型数据集。此外，该研究通过使用类激活映射（CAM）进行解释来满足模型中对安全水平的需求。实验结果表明，所提出的系统对肺癌和结肠癌的诊断准确率高达99.38%。这种高性能凸显了该自动系统在检测这些类型癌症方面的有效性。本研究的结果支持了肺癌和结肠癌早期诊断的潜力，这有助于及时进行治疗干预并改善患者预后。
