Aftab Muhammad, Mehmood Faisal, Sahibzada Kashif Iqbal, Zhang Chengjuan, Jiang Yanan, Liu Kangdong
Pathophysiology Department, School of Basic Medical Sciences, Zhengzhou University, Zhengzhou 450001, China.
Tianjian Laboratory of Advanced Biomedical Sciences, Zhengzhou University, Zhengzhou, Henan 450001, China.
ACS Omega. 2025 Mar 4;10(10):10468-10479. doi: 10.1021/acsomega.4c10763. eCollection 2025 Mar 18.
Accurate detection and segmentation of esophageal lesions are crucial for diagnosing and treating gastrointestinal diseases. However, early detection of esophageal cancer remains challenging, contributing to a reduced five-year survival rate among patients. This paper introduces a novel multitask deep learning model for automatic diagnosis that integrates classification and segmentation tasks to assist endoscopists effectively. Our approach leverages the MobileNetV2 deep learning architecture enhanced with a mutual attention module, significantly improving the model's performance in determining the locations of esophageal lesions. Unlike traditional models, the proposed model is designed not to replace endoscopists but to empower them to correct false predictions when provided with additional Supporting Information. We evaluated the proposed model on three well-known data sets: Early Esophageal Cancer (EEC), CVC-ClinicDB, and KVASIR. The experimental results demonstrate promising performance, achieving high classification accuracies of 98.72% (F1-score: 98.08%) on CVC-ClinicDB, 98.95% (F1-score: 98.32%) on KVASIR, and 99.12% (F1-score: 99.00%) on our generated EEC data set. Compared to state-of-the-art models, our classification results show significant improvement. For the segmentation task, the model attained a Dice coefficient of 92.73% and an Intersection over Union (IoU) of 91.54%. These findings suggest that the proposed multitask deep learning model can effectively assist endoscopists in evaluating esophageal lesions, thereby alleviating their workload and enhancing diagnostic precision.
准确检测和分割食管病变对于胃肠道疾病的诊断和治疗至关重要。然而，食管癌的早期检测仍然具有挑战性，这导致患者的五年生存率降低。本文介绍了一种用于自动诊断的新型多任务深度学习模型，该模型集成了分类和分割任务，以有效协助内镜医师。我们的方法利用了通过相互注意力模块增强的MobileNetV2深度学习架构，显著提高了模型在确定食管病变位置方面的性能。与传统模型不同，所提出的模型并非旨在取代内镜医师，而是在提供额外支持信息时使他们能够纠正错误预测。我们在三个著名的数据集上评估了所提出的模型：早期食管癌（EEC）、CVC-ClinicDB和KVASIR。实验结果显示出有前景的性能，在CVC-ClinicDB上实现了98.72%的高分类准确率（F1分数：98.08%），在KVASIR上为98.95%（F1分数：98.32%），在我们生成的EEC数据集上为99.12%（F1分数：99.00%）。与最先进的模型相比，我们的分类结果有显著改进。对于分割任务，该模型的Dice系数为92.73%，交并比（IoU）为91.54%。这些发现表明，所提出的多任务深度学习模型可以有效地协助内镜医师评估食管病变，从而减轻他们的工作量并提高诊断精度。
