PDF(Pubmed)
Abstract：
Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.
摘要：
癌症,由遗传异常和代谢异常引起的危及生命的疾病，是对健康的重大危害，肺癌和结肠癌是死亡的主要原因。组织病理学鉴定对于指导这些癌症的有效治疗方案至关重要。这些疾病越早被发现，死亡的风险越小。机器学习和深度学习方法的使用有可能通过允许研究人员快速，经济地分析大型患者数据库来加快癌症诊断过程。这项研究引入了Inception-ResNetV2模型，该模型具有战略性地结合了局部二进制模式（LBP）特征，以提高肺癌和结肠癌识别的诊断准确性。该模型是在组织病理学图像上训练的，深度学习和基于纹理的特征的集成已经证明了其卓越的性能，准确率为99.98%。重要的是,该研究通过Shapley加法扩张（SHAP）采用可解释的人工智能（AI）来揭示深度学习模型的复杂内部运作，在决策过程中提供透明度。这项研究强调了在更准确和可靠的医学评估时代彻底改变癌症诊断的潜力。
