Deep ensemble approach for pathogen classification in large-scale images using patch-based training and hyper-parameter optimization.

Pathogenic bacteria present a major threat to human health, causing various infections and illnesses, and in some cases, even death. The accurate identification of these bacteria is crucial, but it can be challenging due to the similarities between different species and genera. This is where automated classification using convolutional neural network (CNN) models can help, as it can provide more accurate, authentic, and standardized results.In this study, we aimed to create a larger and balanced dataset by image patching and applied different variations of CNN models, including training from scratch, fine-tuning, and weight adjustment, and data augmentation through random rotation, reflection, and translation. The results showed that the best results were achieved through augmentation and fine-tuning of deep models. We also modified existing architectures, such as InceptionV3 and MobileNetV2, to better capture complex features. The robustness of the proposed ensemble model was evaluated using two data splits (7:2:1 and 6:2:2) to see how performance changed as the training data was increased from 10 to 20%. In both cases, the model exhibited exceptional performance. For the 7:2:1 split, the model achieved an accuracy of 99.91%, F-Score of 98.95%, precision of 98.98%, recall of 98.96%, and MCC of 98.92%. For the 6:2:2 split, the model yielded an accuracy of 99.94%, F-Score of 99.28%, precision of 99.31%, recall of 98.96%, and MCC of 99.26%. This demonstrates that automatic classification using the ensemble model can be a valuable tool for diagnostic staff and microbiologists in accurately identifying pathogenic bacteria, which in turn can help control epidemics and minimize their social and economic impact.