Microorganism image classification with circle-based Multi-Region Binarization and mutual-information-based feature selection

Automatic detection of Cryptosporidium in optical microscopy images using YOLOv5x: a comparative study

Here, a machine learning tool (YOLOv5) enables the detection of Cryptosporidium microorganisms using optical and phase contrast microscope images. The two databases were processed using 520 images (optical microscopy) and 1200 images (phase contrast microscopy). It used Python libraries to label, standardize the size, and crop the images to generate the input tensors to the YOLOv5 network (s, m, and l). It implemented two experiments using randomly initialized weights in optical and phase contrast microscope images. The other two experiments used the parameters for the best training time obtained before and after retraining the models. Metrics used to assess model accuracy were mean average accuracy, confusion matrix, and the F1 scores. All three metrics confirmed that the optimal model used the best epoch of optical imaging training and retraining with phase contrast imaging. Experiments with randomly initialized weights with optical imaging showed the lowest precision for Cryptosporidium detection. The most stable model was YOLOv5m, with the best results in all categories. However, the differences between all models are lower than 2%, and YOLOv5s is the best option for Cryptosporidium detection considering the differences in computational costs of the models.

Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA's performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA's performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis.