LukeNet：一种集成XAI模型的轻量级CNN，用于智能急性淋巴细胞白血病检测与管理

中文摘要 AI 辅助

急性淋巴细胞白血病（ALL）患者需要早期、准确的检测，以便及时治疗和有效管理患者。卷积神经网络（CNN）非常适合创建用于ALL检测和分类的端到端支持环境。然而，大多数基于CNN的ALL检测系统都是理论性的，由于计算需求高，不适合部署在边缘设备上。医疗物联网（IoMT）设备为实时监测ALL患者提供了机会。跟踪体温、心率、血氧饱和度和活动度的可穿戴设备可以传递关键数据，以支持及时的临床干预并改善患者预后。在智能IoMT环境中，轻量级CNN至关重要，因为连接的设备通常在有限的计算能力、内存和延迟约束下运行。为满足这一需求，本研究提出了LukeNet，一种集成可解释人工智能（XAI）的轻量级CNN，用于基于IoMT的智能急性淋巴细胞白血病检测与管理系统。LukeNet在三个（3个）ALL数据集上训练，并采用五折交叉验证，取得了令人印象深刻的99%模型准确率以及99%未见测试准确率，高于六种最先进的（SOTA）CNN，如DenseNet121、MobileNet、ResNet50、InceptionV3、Xception和VGG16，以及迁移学习模型。此外，LukeNet还与两个集成模型进行了比较。另外，集成了可解释人工智能方法以突出显微图像中的相关区域。本研究的创新之处在于LukeNet的架构，它通过使用深度可分离卷积来平衡模型深度和计算效率，降低了深层中梯度丢失的风险，并提供了强大的全局和局部特征提取能力。

英文摘要

Acute Lymphoblastic Leukemia (ALL) patients require early, accurate detection to enable timely treatment and effective patient management. A Convolutional Neural Network (CNN) is well-suited for creating an end-to-end enabling environment for ALL detection and classification. However, most CNN-based ALL detection systems are theoretical and unsuitable for deployment on edge devices due to high computational demands. The Internet of Medical Things (IoMT)-enabled devices offer an opportunity to monitor ALL patients in real time. Wearables that track temperature, heart rate, oxygen saturation, and activity can deliver critical data to support timely clinical intervention and improve patient outcomes. In smart IoMT environments, a lightweight CNN is essential because connected devices often operate under limited computational power, memory, and latency constraints. To address this need, this study proposes LukeNet, a lightweight CNN integrated with explainable artificial intelligence (XAI) for an IoMT-based SMART Acute Lymphoblastic Leukemia Detection and Management System. Trained on three (3) ALL datasets and five-fold cross-validation, LukeNet achieved an impressive 99% model accuracy as well as 99% unseen test accuracy, which is higher than six state-of-the-art (SOTA) CNNs, such as DenseNet121, MobileNet, ResNet50, InceptionV3, Xception, and VGG16, as well as transfer learning models. Furthermore, LukeNet was compared with two ensemble models. In addition, explainable AI methods are integrated to highlight relevant regions in microscopic images. The novelty of this study lies in the architecture of LukeNet, which balances model depth and computational efficiency by using depthwise separable convolutions, mitigates the risk of gradient loss in deeper layers, and provides strong global and local feature extraction capabilities.

发表机构

• North South University（北南大学）

机构由 AI 辅助整理，请以论文原文为准。