Shahadat Nazmul, Lama Ritika, Nguyen Anna
Department of Computer and Data Sciences, Truman State University, Kirksville, MO 63501, USA.
Cancers (Basel). 2024 Nov 20;16(22):3879. doi: 10.3390/cancers16223879.
Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is harmful. Accurately detecting cancer in a patient's tissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, none of these have had a 100% accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have very harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with squeeze-and-excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100% accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy for around 0.35 million trainable parameters and around 6.4 million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection.
肺癌和结肠癌是全球癌症相关死亡的主要原因之一。早期准确检测这些癌症对于有效治疗和改善患者预后至关重要。错误或不准确的检测是有害的。准确检测患者组织中的癌症对其有效治疗至关重要。虽然分析组织样本复杂且耗时，但深度学习技术使更高效准确地完成这一过程成为可能。因此，研究人员可以在更短时间内、以更低成本研究更多患者。已经开展了许多研究来调查需要强大计算能力和资源的深度学习模型。然而，这些模型中没有一个对这些危及生命的恶性肿瘤具有100%的准确检测率。癌症分类错误或误检测可能会产生非常有害的后果。本研究提出了一种基于带有挤压激励层的一维卷积神经网络的新型轻量级、参数高效且可嵌入移动设备的深度学习模型，用于高效检测肺癌和结肠癌。该模型可从数字病理图像中诊断和分类肺鳞状细胞癌、肺腺癌以及结肠癌。大量实验表明，我们提出的模型在从组织病理学（LC25000）肺癌和结肠癌数据集中检测肺癌、结肠癌以及肺癌合并结肠癌时达到了100%的准确率，对于约35万个可训练参数和约640万次浮点运算而言，这被认为是最佳准确率。与现有结果相比，我们提出的架构在肺癌、结肠癌以及肺癌合并结肠癌检测方面表现出了最先进的性能。
