Advanced CNN Architectures for Pollen Classification: Design and Comprehensive Evaluation

Figures & data

Figure 1. Multi-modal neural network architecture.

Table 1. Feature extractors for each data type. Numbers in the convolution brackets correspond to the number of feature maps and the kernel size, respectively.

Scattered light image	Fluorescence lifetime	Fluorescence spectrum
batch normalization convolution(10, 5) relu activation	batch normalization convolution(70, (1, 7)) relu activation	batch normalization convolution(50, (1, 5)) relu activation
batch normalization convolution(20, 3) dropout(0.5) relu activation	batch normalization convolution(140, (1, 5)) relu activation	batch normalization convolution(100, 3) dropout(0.5) relu activation
	batch normalization convolution(200, 3) dropout(0.5) relu activation

Figure 2. Convolutional blocks of different CNN type.

Figure 3. Data instance of class Betula.

Figure 4. Number of data samples per class (abbreviations defined in the Labeled dataset subsection) before and after preprocessing. The y-axis is limited for visibility since classes Poaceae and spores have 82,143 and 157,753 samples before preprocessing, respectively.

Figure 5. Number of parameters for each network and data type in a logarithmic scale.

Table 2. Performances of the uni-modal networks.

	Accuracy	Precision	Recall	F1 score
Scattered light image
Vanilla CNN	0.438	0.434	0.438	0.436
Best GoogleNet	0.427	0.445	0.427	0.436
Best ResNet	0.478	0.507	0.478	0.493
Wide ResNet	0.394	0.392	0.394	0.393
ResNeXt	0.400	0.414	0.400	0.407
AlexNet	0.460	0.492	0.460	0.476
VGG16	0.432	0.448	0.432	0.440
Fluorescence spectrum
Vanilla CNN	0.434	0.475	0.434	0.454
Best GoogleNet	0.447	0.459	0.447	0.453
Best ResNet	0.461	0.490	0.461	0.475
Wide ResNet	0.411	0.421	0.411	0.416
ResNeXt	0.399	0.438	0.399	0.419
Fluorescence lifetime
Vanilla CNN	0.493	0.489	0.493	0.490
Best GoogleNet	0.398	0.420	0.398	0.409
Best ResNet	0.492	0.510	0.492	0.501
Wide ResNet	0.319	0.320	0.319	0.319
ResNeXt	0.332	0.334	0.332	0.333

Figure 6. F1 score of multi-modal networks for different training setups.

Figure 7. Confusion matrix of the best-performing multi-modal network.

Figure 8. The average fluorescence spectrum data with standard deviation for Carpinus, Juglans, Quercus, and Taxaceae.

Figure 9. Precision, recall, and F1 score for each class (abbreviations defined in the Labeled dataset subsection).