Rapid bacterial colony classification using deep learning

Bacterial colonies infection is one of the causes of bloodstream disease, and it can be a fatality. Therefore, medical diagnoses require fast identification and classification of organisms. Artificial Intelligence with deep learning (DL) can now be developed as a rapid bacterial classification. The research aims to combine deep learning and support vector machines (SVM). The ResNet-101 model of the DL algorithm extracted the image’s features using transfer learning then classified by the SVM classifier. According to the experimental results, this model had 99.61% accuracy, 99.58% recall, 99.58% precision, and 99.97% specificity. The technique presented might enhance clinical decision-making

Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

Widely used traditional supervised deep learning methods require a large number of training samples but often fail to generalize on unseen datasets. Therefore, a more general application of any trained model is quite limited for medical imaging for clinical practice. Using separately trained models for each unique lesion category or a unique patient population will require sufficiently large curated datasets, which is not practical to use in a real-world clinical set-up. Few-shot learning approaches can not only minimize the need for an enormous number of reliable ground truth labels that are labour-intensive and expensive, but can also be used to model on a dataset coming from a new population. To this end, we propose to exploit an optimization-based implicit model agnostic meta-learning (iMAML) algorithm under few-shot settings for medical image segmentation. Our approach can leverage the learned weights from diverse but small training samples to perform analysis on unseen datasets with high accuracy. We show that, unlike classical few-shot learning approaches, our method improves generalization capability. To our knowledge, this is the first work that exploits iMAML for medical image segmentation and explores the strength of the model on scenarios such as meta-training on unique and mixed instances of lesion datasets. Our quantitative results on publicly available skin and polyp datasets show that the proposed method outperforms the naive supervised baseline model and two recent few-shot segmentation approaches by large margins. In addition, our iMAML approach shows an improvement of 2%–4% in dice score compared to its counterpart MAML for most experiments

Classification of Corn Seed Quality Using Convolutional Neural Network with Region Proposal and Data Augmentation

Corn is a commodity in agriculture and essential to human food and animal feed. All components of corn can be utilized and accommodated for the benefit of humans. One of the supporting components is the quality of corn seeds, where specific sources have physiological properties to survive. The problem is how to get information on the quality of corn seeds at agricultural locations and get information through direct visual observations. This research tries to find a solution for classifying corn kernels with high accuracy using a convolutional neural network. It is because in-depth training is used in deep learning. The problem with convolutional neural networks is that the training process takes a long time, depending on the number of layers in the architecture. The research contribution is adding Convex Hull. This method looks for edge points on an object and forms a polygon that encloses that point. It helps increase focus on the convolution multiplication process by removing images on the background. The 34-layer architecture maintains feature maps and uses dropout layers to save computation time. The dataset used is primary data. There are six classes, AR21, Pioner_P35, BISI_18, NK212, Pertiwi, and Betras1—data augmentation techniques to overcome data limitations so that overfitting does not occur. The results of the classification of corn kernels obtained a model with an average accuracy of 99.33%, 99.33% precision, 99.33% recall, and 99.36% F-1 score. The computational training time to obtain the model was 2 minutes 30 seconds. The average error value for MSE is 0.0125, RMSE is 0.118, and MAE is 0.0108. The experimental data testing process has an accuracy ranging from 77% -99%. In conclusion, using the proposal area can improve accuracy by about 0.3% because the focused object helps the convolution process

Multivolume devices, kits and related methods for quantification and detection of nucleic acids and other analytes

Provided are devices comprising multivolume analysis regions, the devices being capable of supporting amplification, detection, and other processes. Also provided are related methods of detecting or estimating the presence nucleic acids, viral levels, and other biological markers of interest

Spacelab Science Results Study

Beginning with OSTA-1 in November 1981 and ending with Neurolab in March 1998, a total of 36 Shuttle missions carried various Spacelab components such as the Spacelab module, pallet, instrument pointing system, or mission peculiar experiment support structure. The experiments carried out during these flights included astrophysics, solar physics, plasma physics, atmospheric science, Earth observations, and a wide range of microgravity experiments in life sciences, biotechnology, materials science, and fluid physics which includes combustion and critical point phenomena. In all, some 764 experiments were conducted by investigators from the U.S., Europe, and Japan. The purpose of this Spacelab Science Results Study is to document the contributions made in each of the major research areas by giving a brief synopsis of the more significant experiments and an extensive list of the publications that were produced. We have also endeavored to show how these results impacted the existing body of knowledge, where they have spawned new fields, and if appropriate, where the knowledge they produced has been applied