Spotlights Session 2
Workshop: S2D-OLAD: From shallow to deep, overcoming limited and adverse data

Boosting Classification Accuracy of Fertile Sperm Cell Images leveraging cDCGAN

Dipam Paul


Drawing inferences from a spermatozoon (Sperm Cell) image based on its morphology is ubiquitous, challenging, and of substantial practical interest. In the present study, we endeavour to deconstruct and demonstrate a framework to distinguish between the binary classes, which constitutes 'Good' (Fertile) and 'Bad' (Infertile) Sperm Cell images. We have selected the DenseNet121 architecture to train our model for this task, the reason for which is examined in Section 2.3. Furthermore, Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) was used to tackle the minority Class imbalance problem, which was heavily prominent in the dataset chosen for this task as seen in Section 2.2. We have hand-picked numerous statistical inferential tests and metrics to validate our model to accentuate the reliability of the obtained results, thus finally formulating and delineating a table based on the respective `Quality Scores' of the test samples provided. With the cDCGAN training data augmentation, the test-set accuracy was recorded to be 86.2%, while the model without cDCGAN scored only 24.3%. The source code for this project can be found at xx location (hidden for double-blind review purposes).