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The transferability of transfer learning model based on ImageNet for medical image classification tasks
- 1 Shandong university
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Abstract
Transfer learning with pretrained weights is commonly based on the ImageNet dataset. However, ImageNet does not contain medical images, leaving the transferability of these pretrained weights for medical image classification an open question. The core purpose of this study is to investigate the impact of transfer learning on the accuracy of medical image classification, utilizing ResNet18, VGG11, AlexNet, and MobileNet, which are four of the most widely used neural network models. Specifically, this study aims to determine whether the incorporation of transfer learning techniques leads to significant improvements in the performance of image classification tasks, as compared to traditional methods that do not utilize transfer learning. The dataset consists of approximately 4,000 chest X-ray images with labels of healthy, COVID, or Viral Pneumonia. The final layer's output neurons of the network’s architecture were revised to three to accommodate the ternary classification task. Preprocessing techniques include downsampling and normalization of the pixel values. By maintaining the same dataset and preprocessing methods, this study compares the performance of the models with and without pretrained weights. The results demonstrate that, compared to not using transfer learning, all four network models converge more quickly and achieve higher validation accuracy in the initial epochs when transfer learning is employed. Furthermore, the models exhibit higher prediction accuracy in the final test set. This study suggests that using transfer learning with pretrained weights based on ImageNet can boost the efficiency of medical image classification tasks.
Keywords
transfer learning, ImageNet pretrained weights, medical image classification
[1]. Mair M D et al 2021 A Systematic Review and Meta-Analysis Comparing the Diagnostic Accuracy of Initial RT-PCR and CT scan in Suspected COVID-19 Patients Br J Radiol 94(1119) 20201039
[2]. Woloshin S Patel N and Kesselheim a S 2020 False Negative Tests for SARS-CoV-2 Infection - Challenges and Implications N Engl J Med 383(6) e38
[3]. Liu J Wang Y He G Wang X and Sun M 2022 Quantitative CT Comparison between COVID-19 and Mycoplasma Pneumonia Suspected as COVID-19: A Longitudinal Study BMC Med Imaging 22(1) 21
[4]. Litjens G Kooi T Bejnordi B E et al 2017 A Survey on Deep Learning in Medical Image Analysis Medical Image Analysis 42 60-88
[5]. Farhan A M Q and Yang S 2023 Automatic Lung Disease Classification from the Chest X-Ray Images Using Hybrid Deep Learning Algorithm Multimed Tools Appl
[6]. LeCun Y Bengio Y and Hinton G 2015 Deep Learning Nature 521(7553) 436-444
[7]. Suganyadevi S Seethalakshmi V and Balasamy K 2022 A Review on Deep Learning in Medical Image Analysis Int J Multimed Info Retr 11 19-38
[8]. Tajbakhsh N Shin J Y Gurudu S R et al 2016 Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine-Tuning? IEEE Transactions on Medical Imaging 35(5) 1299-1312
[9]. Mustafa B et al 2021 Supervised Transfer Learning at Scale for Medical Imaging ArXiv abs/2101.05913
[10]. Kaggle 2022 Convid19 radiography database https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database
[11]. LeCun Y et al 1989 Backpropagation Applied to Handwritten Zip Code Recognition Neural Computation 1(4) 541-551
[12]. Krizhevsky A Sutskever I and Hinton G E 2012 ImageNet Classification with Deep Convolutional Neural Networks Advances in Neural Information Processing Systems (NIPS) 1097-1105
[13]. Howard A G Sandler M Chu G Chen L C Chen B Tan M et al 2019 Searching for MobileNetV3 Proceedings of the IEEE International Conference on Computer Vision (ICCV) 1314-1324
[14]. Simonyan K and Zisserman A 2014 Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv preprint arXiv:1409.1556
[15]. He K Zhang X Ren S and Sun J 2016 Deep Residual Learning for Image Recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 770-778
Cite this article
Zhang,Z. (2023). The transferability of transfer learning model based on ImageNet for medical image classification tasks. Applied and Computational Engineering,18,143-151.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 5th International Conference on Computing and Data Science
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