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Comparison among Four Deep Learning Image Classification Algorithms in AI-based Diatom Test
Yong-Zheng Zhu1,2, Ji Zhang2, Qi Cheng2,3
1School of Forensic Medicine, Shanxi Medical University, Taiyuan 030001, China.
Fa Yi Xue Za Zhi
|June 20, 2022
Summary
The InceptionV3 deep learning model excels at automatic diatom recognition in forensic medicine, offering balanced accuracy and complexity for improved testing. This study identifies it as the most suitable algorithm for forensic diatom examination.
Area of Science:
- Forensic Science
- Computational Biology
- Digital Pathology
Background:
- Automatic diatom recognition is crucial for forensic investigations, particularly in drowning cases.
- Deep learning offers potential for improving the accuracy and efficiency of diatom testing.
Purpose of the Study:
- To evaluate four deep learning algorithms for automatic diatom recognition.
- To identify the most suitable algorithm for forensic diatom analysis.
Main Methods:
- A dataset of 20,000 images of diatoms and background from lung tissue was created.
- Four Convolutional Neural Network (CNN) models (VGG16, ResNet50, InceptionV3, Inception-ResNet-V2) were trained and validated.
- Performance was evaluated using metrics like recall, precision, accuracy, and F1 score.
Main Results:
- InceptionV3 demonstrated superior performance with a recall rate of 89.80% and precision of 92.58%.
- VGG16 and Inception-ResNet-V2 showed comparable, acceptable performance.
- ResNet50 exhibited the lowest performance with a 55.35% recall rate.
Conclusions:
- InceptionV3 is the most effective model for diatom identification and feature extraction.
- The Inception-dependent model's targeted feature extraction makes it ideal for forensic diatom examination.
