比较MobileNetV2与从零训练的CNN在肺炎检测中的表现
Diagnostic Uncertainty in Pneumonia Detection using CNN MobileNetV2 and CNN from Scratch
- 用预训练MobileNetV2和自建CNN模型对比诊断肺炎
- MobileNetV2准确率84.87%且更稳定，过拟合少
- 自建模型初期精度高但后期过拟合严重，适合初学者参考
肺炎诊断对有效治疗至关重要，但常因表现不典型、影像工具局限及共病等因素导致不确定性。本研究采用监督学习方法，使用预训练的MobileNetV2（基于ResNet101V2架构）和Keras API从零构建的CNN模型，识别肺部疾病尤其是肺炎。数据来自Kaggle公开数据集。实验结果显示，训练过程中，MobileNetV2模型准确率提升至84.87%，验证损失由0.499升至0.6345，但仍保持稳定性且过拟合轻微；而自建模型在第10轮后出现明显不稳定，训练准确率降至78.12%，验证损失从0.5698升至1.1809，过拟合显著。总体表明，ResNet101V2结构提供稳定性，自建模型虽初期精度较高但易过拟合。
原文摘要 · Abstract (English)
Pneumonia Diagnosis, though it is crucial for an effective treatment, it can be hampered by uncertainty. This uncertainty starts to arise due to some factors like atypical presentations, limitations of diagnostic tools such as chest X-rays, and the presence of co-existing respiratory conditions. This research proposes one of the supervised learning methods, CNN. Using MobileNetV2 as the pre-trained one with ResNet101V2 architecture and using Keras API as the built from scratch model, for identifying lung diseases especially pneumonia. The datasets used in this research were obtained from the website through Kaggle. The result shows that by implementing CNN MobileNetV2 and CNN from scratch the result is promising. While validating data, MobileNetV2 performs with stability and minimal overfitting, while the training accuracy increased to 84.87% later it slightly decreased to 78.95%, with increasing validation loss from 0.499 to 0.6345. Nonetheless, MobileNetV2 is more stable. Although it takes more time to train each epoch. Meanwhile, after the 10th epoch, the Scratch model displayed more instability and overfitting despite having higher validation accuracy, training accuracy decreased significantly to 78.12% and the validation loss increased from 0.5698 to 1.1809. With these results, ResNet101V2 offers stability, and the Scratch model offers high accuracy.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。