The automatic detection of bone marrow (BM) cell diseases plays a vital role in the medical field; it helps to make diagnoses more precise and effective, which leads to early detection and can significantly improve patient outcomes and increase the chances of successful intervention. This study proposed a fully automated intelligent system for BM classification by developing and enhancing Capsule Neural Network (CapsNet) architecture. Although CapsNet has demonstrated success in many classification tasks, it still has some limitations and challenges associated with using Convolutional Neural Networks (CNNs), which suffer from information loss during the pooling and discarding of detailed spatial information, resulting in the loss of fine‐grained features. Additionally, CNNs must help capture hierarchical feature relationships and often learn them implicitly by stacking convolutional layers. In contrast, CapsNets are designed to capture hierarchical features through dynamic routing and relationships between capsules, resulting in a more explicit representation of spatial hierarchy. CapsNets manage transformations and offer equivariance, preserving spatial information through capsule routing mechanisms. Further, to improve how features are represented, pre‐trained models such as Residual Capsule Network (RES‐CapsNet), Visual Geometry Group Capsule Network (VGG‐CapsNet), and Google Network (Inception V3) (GN‐CapsNet) have been used. This helps the network obtain the low‐ and mid‐level features and information it has previously learned so that subsequent capsule layers receive better initial information. Additionally, the Synthetic Minority Over‐Sampling Technique (SMOTE) was implemented to mitigate class imbalance. It generates synthetic samples in feature space by over‐sampling the minority class, leading to improving model performance in accurately classifying rare instances. Fine‐tuning the hyperparameters and implementing these improvements resulted in remarkable accuracy rates on a large BM dataset, with reduced training time and trainable parameters. CapsNet achieved 96.99%, VGG‐CapsNet achieved 98.95%, RES‐CapsNet achieved 99.24%, and the GN‐CapsNet model demonstrated superior accuracy at 99.45%. GN‐Caps Net was the best because it requires a small number of epochs and has an effective deep inception architecture that efficiently extracts features at different scales to form a robust representation of the input. Our proposed models were compared with existing state‐of‐the‐art models using the BM dataset; the results showed that our models outperformed the existing approaches and demonstrated excellent performance. Further, this automated system can analyze large amounts of data and complex cells in images of the BM dataset. Thus, it gives healthcare professionals a detailed understanding of different diseases, which may take time to achieve manually.

中文翻译：

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检查预训练胶囊网络在不平衡骨髓细胞数据集上的分类性能

骨髓（BM）细胞疾病的自动检测在医学领域发挥着至关重要的作用；它有助于使诊断更加精确和有效，从而实现早期发现，并可以显着改善患者的治疗效果并增加成功干预的机会。本研究通过开发和增强胶囊神经网络（CapsNet）架构，提出了一种用于BM分类的全自动智能系统。尽管 CapsNet 在许多分类任务中取得了成功，但它仍然存在与使用卷积神经网络 (CNN) 相关的一些限制和挑战，卷积神经网络在池化和丢弃详细空间信息过程中会遭受信息丢失，导致细粒度的损失特征。此外，CNN 必须帮助捕获层次特征关系，并经常通过堆叠卷积层来隐式学习它们。相比之下，CapsNet 旨在通过动态路由和胶囊之间的关系来捕获层次结构特征，从而更明确地表示空间层次结构。 CapsNet 管理转换并提供等变性，通过胶囊路由机制保留空间信息。此外，为了改进特征的表示方式，使用了预训练模型，例如残差胶囊网络（RES-CapsNet）、视觉几何组胶囊网络（VGG-CapsNet）和谷歌网络（Inception V3）（GN-CapsNet） 。这有助于网络获得先前学到的低级和中级特征和信息，以便后续胶囊层接收更好的初始信息。此外，还实施了合成少数过采样技术（SMOTE）来缓解类别不平衡。它通过对少数类进行过采样来在特征空间中生成合成样本，从而提高模型在准确分类稀有实例方面的性能。微调超参数并实施这些改进可以在大型 BM 数据集上获得显着的准确率，同时减少训练时间和可训练参数。 CapsNet 达到了 96.99%，VGG-CapsNet 达到了 98.95%，RES-CapsNet 达到了 99.24%，GN-CapsNet 模型表现出了 99.45% 的卓越准确率。 GN-Caps Net 是最好的，因为它需要少量的 epoch，并且具有有效的深度初始架构，可以有效地提取不同尺度的特征以形成输入的鲁棒表示。使用 BM 数据集将我们提出的模型与现有的最先进模型进行比较；结果表明，我们的模型优于现有方法并表现出出色的性能。此外，该自动化系统可以分析 BM 数据集图像中的大量数据和复杂细胞。因此，它使医疗保健专业人员能够详细了解不同的疾病，而这可能需要花费一些时间才能手动实现。