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Food quality assessment is an important part of the food industry. The traditional food quality assessment technologies have the limitations of inconsistent and different technical defects for each method. Data mining technology has significant advantages in dealing with the problems of uncertainty and fuzziness. Therefore, this study proposes a food quality assessment model based on data mining, which aims to realize the standardization and consistency of food quality assessment, and can achieve or exceed the accuracy of existing technologies, so as to solve the obvious problems existing in traditional assessment methods. The core of the proposed model is to design a deep learning framework based on double layer rough-refinement optimization. The first layer is rough optimization, which introduces the thought of multi-objective optimization to optimize the topological structure of neural networks with various candidate types and candidate depths. The second layer is refinement adjustment, which uses meta heuristic algorithm to globally optimize the weight parameters of the network model. The combination of rough and refinement optimization can greatly reduce the computation of overall simultaneous optimization and globally optimize the neural network model with the highest accuracy from the neural network type, topology, and network parameters. Two kinds of food quality assessment problems are used to simulate and verify the proposed deep learning framework. The results prove that the framework is effective, feasible, and adaptability, and the proposed assessment model can well solve different types of food quality assessments. [ABSTRACT FROM AUTHOR]

Lung cancer accounts for the greatest number of cancer-related mortality, while the accurate evaluation of pulmonary nodules in computed tomography (CT) images can significantly increase the 5-year relative survival rate. Despite deep learning methods that have recently been introduced to the identification of malignant nodules, a substantial challenge remains due to the limited datasets. In this study, we propose a cascaded-recalibrated multiple instance learning (MIL) model based on multiattribute features transfer for pathologic-level lung cancer prediction in CT images. This cascaded-recalibrated MIL deep model incorporates a cascaded recalibration mechanism at the nodule level and attribute level, which fuses the informative attribute features into nodule embeddings and then the key nodule features can be converged into the patient-level embedding to improve the performance of lung cancer prediction. We evaluated the proposed cascaded-recalibrated MIL model on the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) benchmark dataset and compared it to the latest approaches. The experimental results showed a significant performance boost by the cascaded-recalibrated MIL model over the higher-order transfer learning, instance-space MIL, and embedding-space MIL models and the radiologists. In addition, the recalibration coefficients of the nodule and attribute feature for the final decision were also analyzed to reveal the underlying relationship between the confirmed diagnosis and its highly-correlated attributes. The cascaded recalibration mechanism enables the MIL model to pay more attention to those important nodules and attributes while suppressing less-useful feature embeddings, and the cascaded-recalibrated MIL model provides substantial improvements for the pathologic-level lung cancer prediction by using the CT images. The identification of the important nodules and attributes also provides better interpretability for model decision-making, which is very important for medical applications. [ABSTRACT FROM AUTHOR]

Background: Pneumothorax (PTX) may cause a life-threatening medical emergency with cardio-respiratory collapse that requires immediate intervention and rapid treatment. The screening and diagnosis of pneumothorax usually rely on chest radiographs. However, the pneumothoraces in chest X-rays may be very subtle with highly variable in shape and overlapped with the ribs or clavicles, which are often difficult to identify. Our objective was to create a large chest X-ray dataset for pneumothorax with pixel-level annotation and to train an automatic segmentation and diagnosis framework to assist radiologists to identify pneumothorax accurately and timely.Methods: In this study, an end-to-end deep learning framework is proposed for the segmentation and diagnosis of pneumothorax on chest X-rays, which incorporates a fully convolutional DenseNet (FC-DenseNet) with multi-scale module and spatial and channel squeezes and excitation (scSE) modules. To further improve the precision of boundary segmentation, we propose a spatial weighted cross-entropy loss function to penalize the target, background and contour pixels with different weights.Results: This retrospective study are conducted on a total of eligible 11,051 front-view chest X-ray images (5566 cases of PTX and 5485 cases of Non-PTX). The experimental results show that the proposed algorithm outperforms the five state-of-the-art segmentation algorithms in terms of mean pixel-wise accuracy (MPA) with [Formula: see text] and dice similarity coefficient (DSC) with [Formula: see text], and achieves competitive performance on diagnostic accuracy with 93.45% and [Formula: see text]-score with 92.97%.Conclusion: This framework provides substantial improvements for the automatic segmentation and diagnosis of pneumothorax and is expected to become a clinical application tool to help radiologists to identify pneumothorax on chest X-rays. [ABSTRACT FROM AUTHOR]

Deep-learning-based regression model composed of stacked auto-encoders (SAE) and fully-connected neural network (FNN) was used for the detection and quantification of nitrogen (N) concentration in oilseed rape leaf. SAE was applied to extract deep spectral features from visible and near-infrared (380–1030 nm) hyperspectral image of oilseed rape leaf, and then these features were used as input data for FNN to predict N concentration. The SAE-FNN model achieved reasonable performance with R 2 P  = 0.903, RMSEP =0 .307% and RPD P  = 3.238 for N concentration. Results confirmed the possibility of rapid and nondestructive detecting N concentration in oilseed rape leaf by the combination of hyperspectral imaging technique and deep learning method. [ABSTRACT FROM AUTHOR]

Reliable and efficient avian monitoring tools are required to identify population change and then guide conservation initiatives. Autonomous recording units (ARUs) could increase both the amount and quality of monitoring data, though manual analysis of recordings is time consuming. Machine learning could help to analyze these audio data and identify focal species, though few ornithologists know how to cater this tool for their own projects. We present a workflow that exemplifies how machine learning can reduce the amount of expert review time required for analyzing audio recordings to detect a secretive focal species (Sora; Porzana carolina). The deep convolutional neural network that we trained achieved a precision of 97% and reduced the amount of audio for expert review by ~66% while still retaining 60% of Sora calls. Our study could be particularly useful, as an example, for those who wish to utilize machine learning to analyze audio recordings of a focal species that has not often been recorded. Such applications could help to facilitate the effective conservation of avian populations. [ABSTRACT FROM AUTHOR]