近年来，非传染性疾病（NCD）需要更多的关注，因为它们需要专门的基础设施进行治疗。根据癌症人口登记处估计，每年将发现近 80 万个新癌症病例。统计数据提醒人们需要进行早期癌症检测和诊断。癌症识别可以通过手动或计算机辅助算法进行。基于人工的癌症检测是劳动密集型的，而且时间复杂度也更高。相比之下，计算机辅助算法提供了减少时间和手动工作的可行性。出于开发非传染性疾病计算机辅助诊断系统的动机，我们开发了一种癌症检测方法。在本文中，开发了一种基于深度学习（DL）的癌症识别模型。在基于深度学习的架构中，通常使用卷积神经网络来提取特征。所提出的注意力引导、密集连接残差和扩张卷积深度神经网络（称为 DeepHistoNet）可以获得精确的分类模式。在 Kasturba 医学院（KMC）、TCGA-LIHC 和 LC25000 数据集上进行了实验，证明了模型的稳健性。 F1 分数、灵敏度、特异性、召回率和准确性等性能评估指标验证了实验。实验结果表明，所提出的 DeepHistoNet 模型优于其他最先进的方法。所提出的模型能够以 97.1% 的准确率和 0.9867 的曲线接收者工作特征曲线下面积值 (AUC-ROC) 对 KMC 肝脏数据集进行分类，这是与现有模型相比获得的最佳结果。艺术技巧。 DeepHistoNet 在 LC25000 数据集上的性能甚至更好。在LC25000数据集上，所提出的模型实现了99.8%的分类准确率。据我们所知，DeepHistoNet 是一种用于多种组织病理学图像分类的新颖方法。研究亮点：提出了一种用于组织病理学图像癌症分类的新型鲁棒深度学习模型。使用密集的交叉连接残差块提取用于准确分类的精确模式。为网络提供空间注意力，以便在特征提取过程中不会丢失空间信息。 DeepHistoNet 在肝脏、肺和结肠组织病理学数据集上进行训练和评估，以证明其弹性。结果很有希望，并且优于最先进的技术。所提出的方法在 KMC 数据集上获得了 0.9867 的 AUC-ROC 值，分类准确率为 97.1%。所提出的 DeepHistoNet 对 LC25000 数据集进行了分类，准确率达到 99.8%。这是迄今为止获得的最佳结果。© 2023 Wiley periodicals LLC。

In recent days, non-communicable diseases (NCDs) require more attention since they require specialized infrastructure for treatment. As per the cancer population registry estimate, nearly 800,000 new cancer cases will be detected yearly. The statistics alarm the need for early cancer detection and diagnosis. Cancer identification can be made either through manual efforts or by computer-aided algorithms. Manual efforts-based cancer detection is labor intensive and also offers more time complexity. In contrast, computer-aided algorithms offer feasibility in reducing time and manual efforts. With the motivation to develop a computer-aided diagnosis system for NCD, we developed a cancer detection methodology. In the present article, a deep learning (DL)-based cancer identification model is developed. In DL-based architectures, the features are generally extracted using convolutional neural networks. The proposed attention-guided, densely connected residual, and dilated convolution deep neural network called DeepHistoNet acquire precise patterns for classification. Experimentation has been carried out on Kasturba Medical College (KMC), TCGA-LIHC, and LC25000 datasets to prove the robustness of the model. Performance evaluation metrics like F1-score, sensitivity, specificity, recall, and accuracy validate the experimentation. Experimental results demonstrate that the proposed DeepHistoNet model outperforms the other state-of-the-art methods. The proposed model has been able to classify the KMC liver dataset with 97.1% accuracy and 0.9867 value of area under the curve-receiver operating characteristic curve (AUC-ROC), which is the best result obtained compared to the state-of-the-art techniques. The performance of the DeepHistoNet has been even better on the LC25000 dataset. On the LC25000 dataset, the proposed model achieved 99.8% classification accuracy. To our knowledge, DeepHistoNet is a novel approach for multiple histopathological image classification. RESEARCH HIGHLIGHTS: A novel robust DL model is proposed for histopathological image carcinoma classification. The precise patterns for accurate classification are extracted using dense cross-connected residual blocks. Spatial attention is provided to the network so that the spatial information is not lost during the feature extraction. DeepHistoNet is trained and evaluated on the liver, lung, and colon histopathology datasets to demonstrate its resilience. The results are promising and outperform state-of-the-art techniques. The proposed methodology has obtained the AUC-ROC value of 0.9867 with a classification accuracy of 97.1% on the KMC dataset. The proposed DeepHistoNet has classified the LC25000 dataset with 99.8% accuracy. The results are the best obtained till date.© 2023 Wiley Periodicals LLC.