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 a100%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 achieves100%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 around0.35million trainable parameters and around6.4million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection.
肺癌与结肠癌是全球范围内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具有危害性。准确检测患者组织中的癌症对其有效治疗极为关键。虽然组织样本分析过程复杂且耗时，但深度学习技术使得这一过程能够更高效、更准确地完成。因此，研究人员能够在更短的时间内以更低的成本研究更多患者。已有大量研究探索需要强大计算能力和资源的深度学习模型，然而这些模型均未能实现对这类致命性恶性肿瘤达到100%的检测准确率。癌症的误分类或错误检测可能产生极其严重的后果。本研究提出一种基于一维卷积神经网络并结合压缩激励层的新型轻量化、参数高效、可嵌入移动设备的深度学习模型，用于高效检测肺癌与结肠癌。该模型能够通过数字病理图像诊断和分类肺鳞状细胞癌、肺腺癌及结肠腺癌。大量实验表明，我们提出的模型在组织病理学（LC25000）肺与结肠数据集上实现了肺癌、结肠癌及两者联合检测的100%准确率，这在约35万个可训练参数和约640万浮点运算量的条件下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、结肠癌及两者联合检测方面展现出最先进的性能。
肿瘤(癌症)患者之家