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There are two main problems with finding mi croplastics in environmental samples: (1) telling plastic particles apart from organic matter and (2) getting results that domain experts can understand and check. Existing methods are very accurate, but they don’t explain how they work and often don’t make a distinction between plastic and non-plastic. We put forward a two-stage framework that is easy to understand and combines YOLOv8 object detection with morphological feature extraction for pre-screening tasks. We explicitly include Algae as a negative class, which is different from previous work. This is because there is a lot of confusion in the real world between clear plastics and biological material. Our system gets 87.5% recall and 91.7% precision on a 4-class dataset. This is 19.5 points better than image-level baselines. An analysis of feature importance shows that the most useful geometric descriptors are equivalent diameter, eccentricity, and convex hull ratio. Our framework makes it possible to do cost-effective pre-screening that cuts down on FTIR testing by 60% while keeping the false negative rate below 13%. It does this by giving contamination index quantification and interpretable feature-based decisions.
Electroencephalography (EEG) clinical tool is widely used for the diagnosis of epilepsy. This tool is able to capture the electrical activity of the brain. Automated computer aided diagnosis (CAD) systems, based on deep learning have gaining attention as Conventional EEG analysis are time-consuming as it is based on neurologists manually inspecting to identify epileptiform abnormalities. The conventional method is also affected by artifacts, and subjected to inter observer variability. In the current work, we classify the EEG using a one dimensional convolutional neural network (CNN) having 13 layers and expand this CNN with a controlled ablation framework (A0-A6). A single channel EEG segments from the Bonn University EEG dataset are used for this paper, with preprocessing the dataset, which included fixed length segmentation (4097 samples) and per segment z score normalization, without feature extraction. By plusging the best of all the mentioned models, with residual learning, data augmentation, attention mechanism, multi domain feature fusion, self supervised pretraining, is the best performing model, which yields the highest classification performance under ten fold cross validation. The candidates of the variants are compared with each other by applying the same training setup with Adam optimizer and stratified 10 fold cross validation. Results demonstrate that both the reproduced baseline CNN model and the proposed improved models, obtain 96.00% ± 2.49% accuracy, respectively, thus the proposed improved CNN models boost the performance of baseline CNN model. The model with residual learning, data augmentation and attention obtained the best classification accuracy, sensitivity and specificity, which was 100%, 100% and 100% respectively. 99.33% ± 1.33% Accuracy with Multi Domain Fusion and Self Supervised Pretraining. The proposed framework is reproducible, modular and appropriate for automatic classification of EEG based epileptic signal.
There are two main problems with finding mi croplastics in environmental samples: (1) telling plastic particles apart from organic matter and (2) getting results that domain experts can understand and check. Existing methods are very accurate, but they don’t explain how they work and often don’t make a distinction between plastic and non-plastic. We put forward a two-stage framework that is easy to understand and combines YOLOv8 object detection with morphological feature extraction for pre-screening tasks. We explicitly include Algae as a negative class, which is different from previous work. This is because there is a lot of confusion in the real world between clear plastics and biological material. Our system gets 87.5% recall and 91.7% precision on a 4-class dataset. This is 19.5 points better than image-level baselines. An analysis of feature importance shows that the most useful geometric descriptors are equivalent diameter, eccentricity, and convex hull ratio. Our framework makes it possible to do cost-effective pre-screening that cuts down on FTIR testing by 60% while keeping the false negative rate below 13%. It does this by giving contamination index quantification and interpretable feature-based decisions.
There are two main problems with finding mi croplastics in environmental samples: (1) telling plastic particles apart from organic matter and (2) getting results that domain experts can understand and check. Existing methods are very accurate, but they don’t explain how they work and often don’t make a distinction between plastic and non-plastic. We put forward a two-stage framework that is easy to understand and combines YOLOv8 object detection with morphological feature extraction for pre-screening tasks. We explicitly include Algae as a negative class, which is different from previous work. This is because there is a lot of confusion in the real world between clear plastics and biological material. Our system gets 87.5% recall and 91.7% precision on a 4-class dataset. This is 19.5 points better than image-level baselines. An analysis of feature importance shows that the most useful geometric descriptors are equivalent diameter, eccentricity, and convex hull ratio. Our framework makes it possible to do cost-effective pre-screening that cuts down on FTIR testing by 60% while keeping the false negative rate below 13%. It does this by giving contamination index quantification and interpretable feature-based decisions.