Deep Learning-Based Morphological Classification of Human Sperm Heads
Abstract
:1. Introduction
2. Related Work
3. Methodology
3.1. Datasets Descrption, Partitioning, and Augmentation
3.2. Proposed Deep CNN Architecture and Learning Paradigm
4. Experimental Results
4.1. On the Stratified Five-Fold Partition of the SCIAN Dataset with the Partial Agreement Setting
4.2. On the Stratified Five-Fold Partition of the SCIAN Dataset with the Total Agreement Setting
4.3. On the Stratified Five-Fold Partition of the HuSHeM Dataset
5. Discussion and Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Fold | Set | Sperm Head Classes | Total | ||||
---|---|---|---|---|---|---|---|
Normal | Tapered | Pyriform | Amorphous | Small | |||
1 | Train | 80–20 (4860) | 182–46 (4896) | 60–15 (4860) | 525–131 (4728) | 58–14 (4840) | 905–226 (24184) |
Test | 20 | 46 | 16 | 131 | 14 | 227 | |
2 and 3 | Train | 80 (6400) | 182 (6370) | 61 (6405) | 525 (6300) | 58 (6380) | 906 (31855) |
Test | 20 | 46 | 15 | 131 | 14 | 226 | |
4 | Train | 80 (6240) | 183 (6222) | 61 (6283) | 525 (6300) | 57 (6270) | 906 (31315) |
Test | 20 | 45 | 15 | 131 | 15 | 226 | |
5 | Train | 80 (6240) | 183 (6222) | 61 (6283) | 524 (6288) | 57 (6270) | 905 (31303) |
Test | 20 | 45 | 15 | 132 | 15 | 227 |
Fold | Set | Sperm Head Classes | Total | ||||
---|---|---|---|---|---|---|---|
Normal | Tapered | Pyriform | Amorphous | Small | |||
1 and 2 | Train | 93 (7719) | 214 (7704) | 74 (7696) | 604 (7852) | 70 (7700) | 1055 (38671) |
Test | 7 | 14 | 2 | 52 | 2 | 77 | |
3 | Train | 93 (7719) | 214 (7704) | 75 (7725) | 604 (7852) | 70 (7700) | 1056 (38700) |
Test | 7 | 14 | 1 | 52 | 2 | 76 | |
4 | Train | 93 (7719) | 214 (7704) | 75 (7725) | 603 (7839) | 70 (7700) | 1055 (38687) |
Test | 7 | 14 | 1 | 53 | 2 | 77 | |
5 | Train | 93 (7626) | 215 (7525) | 75 (7575) | 603 (7839) | 69 (7590) | 1055 (38155) |
Test | 7 | 13 | 1 | 53 | 3 | 77 |
Fold | Set | Sperm Head Classes | Total | |||
---|---|---|---|---|---|---|
Normal | Tapered | Pyriform | Amorphous | |||
1, 2 and 3 | Train | 43 (4730) | 42 (4620) | 46 (5060) | 42 (4620) | 173 (19030) |
Test | 11 | 11 | 11 | 10 | 43 | |
4 | Train | 43 (4730) | 43 (4730) | 45 (4950) | 41 (4510) | 172 (18920) |
Test | 11 | 10 | 12 | 11 | 44 | |
5 | Train | 44 (4840) | 43 (4730) | 45 (4950) | 41 (4510) | 173 (19030) |
Test | 10 | 10 | 12 | 11 | 43 |
Model | True Positive Rate | Accuracy (Weighted Average TPR) | Recall (Average TPR) | Precision (Macro) | Specificity (Macro) | F1-Score (Macro) | ||||
---|---|---|---|---|---|---|---|---|---|---|
Normal | Tapered | Pyriform | Amorphous | Small | ||||||
MorphoSpermGS (SVM with Zernike moments) [14] | 44 | 62 | 33 | 23 | 70 | 36 | 46 | - | - | - |
MorphoSpermGS (SVM with Fourier descriptors) [14] | 57 | 68 | 53 | 15 | 54 | 34 | 49 | - | - | - |
CE-SVM [19] | 62 | 64 | 50 | 30 | 82 | 44 | 58 | - | - | - |
APDL [20] | 71 | 67 | 71 | 35 | 68 | 49 | 62 | - | - | - |
FT-VGG [21] | 67 | 57 | 69 | 38 | 78 | 49 | 62 | 47 | 87 | 53 |
Proposed model (MC-HSH) | 70 | 79 | 62 | 57 | 71 | 63 | 68 | 56 | 90 | 61 |
True Class | Normal | 70 | 3 | 3 | 20 | 4 |
Tapered | 2 | 79 | 5 | 13 | 1 | |
Pyriform | 3 | 8 | 62 | 26 | 1 | |
Amorphous | 10 | 16 | 8 | 57 | 9 | |
Small | 10 | 2 | 1 | 16 | 71 | |
Normal | Tapered | Pyriform | Amorphous | Small | ||
Predicted Class |
Fold | Run | Precision | Recall | Specificity | F1-Score | Jaccard | G-mean | ROC-AUC | PR-AUC | MCC | CKS | Evaluation Time per Image (milliseconds) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Micro | Micro | |||||
Accuracy | |||||||||||||||||||
1 | First | 57 | 71 | 67 | 67 | 90 | 86 | 60 | 67 | 44 | 51 | 77 | 75 | 88 | 90 | 69 | +0.52 | +0.50 | ~0.2 |
Second | 54 | 70 | 67 | 63 | 90 | 87 | 58 | 64 | 41 | 48 | 77 | 74 | 87 | 89 | 63 | +0.48 | +0.47 | ||
Third | 53 | 70 | 66 | 63 | 90 | 87 | 56 | 64 | 40 | 46 | 76 | 74 | 87 | 89 | 64 | +0.48 | +0.46 | ||
2 | First | 59 | 72 | 73 | 65 | 90 | 88 | 63 | 66 | 47 | 49 | 81 | 75 | 89 | 90 | 69 | +0.52 | +0.50 | |
Second | 58 | 72 | 73 | 65 | 90 | 87 | 63 | 66 | 46 | 50 | 81 | 76 | 88 | 90 | 67 | +0.52 | +0.50 | ||
Third | 62 | 72 | 71 | 67 | 91 | 86 | 65 | 68 | 48 | 51 | 80 | 76 | 89 | 91 | 72 | +0.53 | +0.52 | ||
3 | First | 50 | 66 | 63 | 56 | 88 | 86 | 52 | 56 | 36 | 39 | 73 | 68 | 85 | 84 | 53 | +0.43 | +0.39 | |
Second | 50 | 68 | 63 | 54 | 88 | 88 | 52 | 55 | 35 | 38 | 74 | 68 | 84 | 84 | 51 | +0.42 | +0.38 | ||
Third | 47 | 63 | 62 | 52 | 87 | 85 | 50 | 53 | 34 | 36 | 73 | 66 | 83 | 83 | 48 | +0.38 | +0.35 | ||
4 | First | 58 | 71 | 69 | 65 | 90 | 86 | 62 | 66 | 45 | 49 | 79 | 75 | 89 | 91 | 72 | +0.51 | +0.49 | |
Second | 62 | 72 | 69 | 69 | 91 | 84 | 65 | 70 | 48 | 54 | 79 | 76 | 89 | 91 | 73 | +0.54 | +0.53 | ||
Third | 63 | 73 | 68 | 70 | 91 | 86 | 65 | 71 | 48 | 55 | 78 | 77 | 90 | 92 | 75 | +0.55 | +0.54 | ||
5 | First | 56 | 70 | 68 | 65 | 90 | 85 | 60 | 66 | 43 | 50 | 78 | 74 | 88 | 89 | 63 | +0.50 | +0.48 | |
Second | 58 | 71 | 68 | 67 | 90 | 84 | 61 | 68 | 45 | 52 | 78 | 75 | 88 | 89 | 62 | +0.51 | +0.50 | ||
Third | 55 | 70 | 69 | 63 | 90 | 86 | 59 | 64 | 42 | 47 | 79 | 73 | 87 | 87 | 58 | +0.49 | +0.47 | ||
Average | 56 | 70 | 68 | 63 | 90 | 86 | 59 | 64 | 43 | 48 | 78 | 73 | 87 | 89 | 64 | +0.49 | +0.47 | ||
Standard deviation | 0.0470 | 0.0263 | 0.0331 | 0.0533 | 0.0116 | 0.0122 | 0.0494 | 0.0539 | 0.0475 | 0.0572 | 0.0259 | 0.0336 | 0.0199 | 0.0282 | 0.0835 | 0.0478 | 0.0561 |
Model | True Positive Rate | Accuracy (Weighted Average TPR) | Recall (Average TPR) | Precision (Macro) | Specificity (Macro) | F1-Score (Macro) | ||||
---|---|---|---|---|---|---|---|---|---|---|
Normal | Tapered | Pyriform | Amorphous | Small | ||||||
CE-SVM [19] | 74 | 70 | 92 | 30 | 100 | 46 | 73 | - | - | - |
FT-VGG [21] | 72 | 67 | 95 | 44 | 84 | 53 | 72 | 45 | 90 | 55 |
Proposed model (MC-HSH) | 80 | 86 | 100 | 72 | 100 | 77 | 88 | 64 | 94 | 74 |
True Class | Normal | 80 | 0 | 3 | 10 | 7 |
Tapered | 2 | 86 | 0 | 9 | 3 | |
Pyriform | 0 | 0 | 100 | 0 | 0 | |
Amorphous | 8 | 11 | 2 | 72 | 7 | |
Small | 0 | 0 | 0 | 0 | 100 | |
Normal | Tapered | Pyriform | Amorphous | Small | ||
Predicted Class |
Fold | Run | Precision | Recall | Specificity | F1-Score | Jaccard | G-mean | ROC-AUC | PR-AUC | MCC | CKS | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Micro | Micro | ||||
Accuracy | ||||||||||||||||||
1 | First | 69 | 88 | 82 | 73 | 94 | 97 | 68 | 77 | 57 | 64 | 87 | 84 | 95 | 94 | 77 | +0.61 | +0.56 |
Second | 62 | 85 | 88 | 70 | 93 | 96 | 67 | 72 | 52 | 57 | 90 | 81 | 96 | 93 | 75 | +0.60 | +0.54 | |
Third | 62 | 87 | 83 | 75 | 94 | 96 | 66 | 79 | 52 | 67 | 88 | 85 | 94 | 93 | 75 | +0.62 | +0.59 | |
2 | First | 69 | 82 | 83 | 78 | 93 | 85 | 72 | 79 | 61 | 67 | 87 | 81 | 94 | 95 | 82 | +0.61 | +0. 60 |
Second | 60 | 84 | 87 | 77 | 93 | 90 | 65 | 79 | 50 | 66 | 89 | 83 | 94 | 94 | 81 | +0.62 | +0.60 | |
Third | 62 | 80 | 83 | 77 | 92 | 94 | 68 | 77 | 52 | 64 | 87 | 80 | 95 | 95 | 85 | +0.59 | +0.58 | |
3 | First | 62 | 83 | 91 | 76 | 92 | 91 | 71 | 77 | 56 | 63 | 92 | 83 | 95 | 94 | 77 | +0.63 | +0.60 |
Second | 64 | 84 | 87 | 80 | 93 | 87 | 72 | 81 | 57 | 69 | 90 | 83 | 95 | 94 | 79 | +0.65 | +0.64 | |
Third | 57 | 84 | 89 | 74 | 94 | 94 | 64 | 75 | 49 | 61 | 91 | 83 | 94 | 93 | 73 | +0.61 | +0.57 | |
4 | First | 60 | 87 | 89 | 79 | 95 | 95 | 67 | 81 | 52 | 69 | 92 | 87 | 97 | 96 | 86 | +0.66 | +0.64 |
Second | 70 | 81 | 85 | 77 | 92 | 86 | 75 | 78 | 63 | 65 | 89 | 81 | 95 | 95 | 82 | +0.59 | +0.58 | |
Third | 66 | 86 | 91 | 79 | 95 | 92 | 74 | 80 | 59 | 68 | 92 | 85 | 97 | 95 | 82 | +0.66 | +0.64 | |
5 | First | 59 | 86 | 92 | 77 | 94 | 95 | 68 | 78 | 53 | 64 | 93 | 85 | 97 | 96 | 84 | +0.66 | +0.62 |
Second | 67 | 89 | 94 | 79 | 95 | 97 | 75 | 80 | 61 | 68 | 94 | 87 | 98 | 96 | 86 | +0.70 | +0.66 | |
Third | 67 | 87 | 94 | 82 | 96 | 95 | 76 | 83 | 61 | 71 | 94 | 88 | 98 | 96 | 86 | +0.71 | +0.69 | |
Average | 64 | 85 | 88 | 77 | 94 | 93 | 70 | 78 | 56 | 66 | 90 | 84 | 96 | 95 | 81 | + 0.63 | +0.61 | |
Standard deviation | 0.0404 | 0.0261 | 0.0405 | 0.0300 | 0.0123 | 0.0401 | 0.0394 | 0.0267 | 0.0456 | 0.0356 | 0.0247 | 0.0243 | 0.0145 | 0.0112 | 0.0442 | 0.0374 | 0.0421 |
Model | True Positive Rate | Accuracy | Recall | Precision | Specificity | F1-Score | |||
---|---|---|---|---|---|---|---|---|---|
Normal | Tapered | Pyriform | Amorphous | ||||||
CE-SVM [19] | 75.9 | 77.3 | 85.9 | 75.0 | 78.5 | 78.5 | 80.5 | 92.9 | 78.9 |
APDL [20] | 94.4 | 94.3 | 87.7 | 94.2 | 92.2 | 92.3 | 93.5 | 97.5 | 92.9 |
FT-VGG [21] | 96.4 | 94.5 | 92.3 | 93.2 | 94.0 | 94.1 | 94.7 | 98.1 | 94.1 |
Proposed model (MC-HSH) | 95.8 | 94.5 | 96.6 | 96.4 | 95.7 | 95.5 | 96.1 | 98.5 | 95.5 |
True Class | Normal | 96 | 3 | 1 | 0 |
Tapered | 1 | 94 | 2 | 3 | |
Pyriform | 0 | 1 | 97 | 2 | |
Amorphous | 2 | 2 | 0 | 96 | |
Normal | Tapered | Pyriform | Amorphous | ||
Predicted Class |
Fold | Run | Precision | Recall | Specificity | F1-Score | Jaccard | G-mean | ROC-AUC | PR-AUC | MCC | CKS | Evaluation Timeper Image (milliseconds) | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Weighted | Macro | Micro | Micro | |||||
Accuracy | |||||||||||||||||||
1 | First | 98 | 98 | 98 | 98 | 99 | 99 | 98 | 98 | 96 | 96 | 98 | 98 | 100 | 100 | 100 | +0.97 | +0.97 | ~0.9 |
Second | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | +1.00 | +1.00 | ||
Third | 98 | 98 | 98 | 98 | 99 | 99 | 98 | 98 | 96 | 96 | 98 | 98 | 100 | 100 | 100 | +0.97 | +0.97 | ||
2 | First | 95 | 96 | 95 | 95 | 98 | 98 | 95 | 95 | 91 | 91 | 97 | 97 | 100 | 99 | 98 | +0.94 | +0.94 | |
Second | 95 | 96 | 95 | 95 | 98 | 98 | 95 | 95 | 91 | 91 | 97 | 97 | 100 | 100 | 99 | +0.94 | +0.94 | ||
Third | 93 | 93 | 93 | 93 | 98 | 98 | 93 | 93 | 87 | 87 | 95 | 95 | 100 | 99 | 99 | +0.91 | +0.91 | ||
3 | First | 95 | 96 | 95 | 95 | 98 | 98 | 95 | 95 | 91 | 91 | 97 | 97 | 100 | 99 | 99 | +0.94 | +0.94 | |
Second | 95 | 96 | 95 | 95 | 98 | 98 | 95 | 95 | 91 | 91 | 97 | 97 | 99 | 99 | 98 | +0.94 | +0.94 | ||
Third | 95 | 96 | 95 | 95 | 98 | 98 | 95 | 95 | 91 | 91 | 97 | 97 | 100 | 99 | 98 | +0.94 | +0.94 | ||
4 | First | 98 | 98 | 98 | 98 | 99 | 99 | 98 | 98 | 95 | 96 | 98 | 98 | 100 | 100 | 100 | +0.97 | +0.97 | |
Second | 95 | 95 | 95 | 95 | 99 | 99 | 95 | 95 | 91 | 91 | 97 | 97 | 100 | 100 | 100 | +0.94 | +0.94 | ||
Third | 95 | 96 | 95 | 95 | 99 | 99 | 95 | 95 | 91 | 92 | 96 | 97 | 100 | 100 | 100 | +0.94 | +0.94 | ||
5 | First | 94 | 94 | 93 | 93 | 98 | 98 | 93 | 93 | 87 | 87 | 95 | 95 | 100 | 100 | 100 | +0.91 | +0.91 | |
Second | 95 | 96 | 96 | 95 | 99 | 99 | 95 | 95 | 91 | 91 | 97 | 97 | 100 | 99 | 98 | +0.94 | +0.94 | ||
Third | 94 | 94 | 93 | 93 | 98 | 98 | 93 | 93 | 87 | 87 | 95 | 95 | 100 | 99 | 99 | +0.91 | +0.91 | ||
Average | 96 | 96 | 96 | 96 | 99 | 99 | 96 | 96 | 92 | 92 | 97 | 97 | 100 | 100 | 99 | +0.94 | +0.94 | ||
Standard deviation | 0.0191 | 0.0181 | 0.0207 | 0.0207 | 0.0064 | 0.0064 | 0.0207 | 0.0207 | 0.0365 | 0.0372 | 0.0133 | 0.0131 | 0.0026 | 0.0052 | 0.0086 | 0.0250 | 0.0250 |
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Iqbal, I.; Mustafa, G.; Ma, J. Deep Learning-Based Morphological Classification of Human Sperm Heads. Diagnostics 2020, 10, 325. https://doi.org/10.3390/diagnostics10050325
Iqbal I, Mustafa G, Ma J. Deep Learning-Based Morphological Classification of Human Sperm Heads. Diagnostics. 2020; 10(5):325. https://doi.org/10.3390/diagnostics10050325
Chicago/Turabian StyleIqbal, Imran, Ghulam Mustafa, and Jinwen Ma. 2020. "Deep Learning-Based Morphological Classification of Human Sperm Heads" Diagnostics 10, no. 5: 325. https://doi.org/10.3390/diagnostics10050325