TITLE:
Classification of Poikilocytes Using ResNet18 and MobileNetV2 Optimized by a Multi-Objective Approach (Pareto Front and TOPSIS)
AUTHORS:
Patrice Kouamé Koffi, Sié Ouattara, Joseph Wognin Vangah, Alain Clement, Nango Jerôme Alico
KEYWORDS:
Red Blood Cell Classification, ResNet18, MobileNetV2, Deep Learning, Optimizers, SGDM, Adam, 5-Fold Stratified Cross-Validation, Multi-Objective Optimization, Pareto Front, TOPSIS
JOURNAL NAME:
Advances in Bioscience and Biotechnology,
Vol.17 No.10,
October
16,
2026
ABSTRACT: Morphological and colorimetric analysis of red blood cells from blood smears constitutes an essential step of hematological diagnosis, yet it remains a lengthy and subjective task that is strongly dependent on the observer’s expertise. To address these limitations, we propose an automated approach combining two (02) pre-trained deep convolutional neural networks, ResNet18 and MobileNetV2, for the classification of five (05) pathological erythrocyte morphologies (acanthocytes, drepanocytes, elliptocytes, ovalocytes and echinocytes). The database used comprises 560 red-blood-cell image patches extracted from blood smears, pre-processed and resized to 224 × 224 × 3 pixels. Training relies on a 5-fold stratified cross-validation and two optimizers, SGDM and Adam, whose hyperparameters (number of epochs, learning rate, batch size) are selected through a grid search followed by a multi-objective optimization combining the Pareto Front and the TOPSIS method on a seven (07)-criterion vector integrating accuracy (F1, MCC, AUC), generalization gaps (GapAcc, GapLoss) and computational cost (number of parameters, latency). The results show that the four retained configurations achieve high and broadly comparable performance (F1 > 0.95; MCC > 0.93; AUC > 0.995); moreover, the model-optimizer pairs (ResNet18, Adam) and (MobileNetV2, Adam) provide the best overall compromise (F1 ≈ 0.966). Beyond the above performance, ResNet18 offers the best generalization robustness, while MobileNetV2, with nearly five times fewer parameters, constitutes a relevant alternative for resource-constrained environments, although its latency measured in practice remains, counter-intuitively, higher than that of ResNet18. These results confirm the value of transfer learning and multi-objective optimization for developing robust, interpretable and reproducible erythrocyte classification systems, while identifying additional validation avenues (per-class confusion matrix, statistical tests) that are necessary before clinical deployment.