scholarly journals Application and Research of Convolution Neural Network in MRI Image Classification and Recognition

2021 ◽  
Vol 2078 (1) ◽  
pp. 012034
Author(s):  
Xuemei Hou ◽  
Fei Gao ◽  
Jianping Wu ◽  
Minghui Wu
Keyword(s):  
Operating Characteristic ◽  
Liver Tumors ◽  
Learning Algorithm ◽  
Experimental Results ◽  
Data Set ◽  
Test Set ◽  
Deep Learning Algorithm ◽  
Test Sets ◽  
Feature Expression ◽  
Mri Image

Abstract The traditional hepaticcell carcinoma (HCC) pathological grading depends on biopsy, which will cause damage to the patient's body and is not suitable for everyone's pathological grading diagnosis. The purpose of this paper is to study the pathological grading of liver tumors on MRI images by using deep learning algorithm, so as to further improve the accuracy of HCC pathological grading. An improved network model based on SE-DenseNet is proposed. The nonlinear mapping relationship between feature channels is modeled and recalibrated using attention mechanism, and rich deep-seated features are extracted, so as to improve the feature expression ability of the network. The method proposed in this paper is verified on the data set including 197 patients, including 130 training sets and 67 test sets. The experimental results are evaluated by receiver operating characteristic (ROC) and area under the ROC curve (AUC). The improved SE-Densenet network achieves good results, and AUC 0.802 is obtained on the test set. The experimental results show that the method proposed in this paper can well predict the pathological grade of HCC.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

scholarly journals Information-Theoretic Generalization Bounds for Meta-Learning and Applications

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 126
Author(s):  
Sharu Theresa Jose ◽  
Osvaldo Simeone
Keyword(s):  
Learning Algorithm ◽  
Broad Class ◽  
Performance Measure ◽  
Training Data ◽  
Learning To Learn ◽  
Data Set ◽  
Information Theoretic ◽  
Meta Learning ◽  
Task Training ◽  
Test Sets

Meta-learning, or “learning to learn”, refers to techniques that infer an inductive bias from data corresponding to multiple related tasks with the goal of improving the sample efficiency for new, previously unobserved, tasks. A key performance measure for meta-learning is the meta-generalization gap, that is, the difference between the average loss measured on the meta-training data and on a new, randomly selected task. This paper presents novel information-theoretic upper bounds on the meta-generalization gap. Two broad classes of meta-learning algorithms are considered that use either separate within-task training and test sets, like model agnostic meta-learning (MAML), or joint within-task training and test sets, like reptile. Extending the existing work for conventional learning, an upper bound on the meta-generalization gap is derived for the former class that depends on the mutual information (MI) between the output of the meta-learning algorithm and its input meta-training data. For the latter, the derived bound includes an additional MI between the output of the per-task learning procedure and corresponding data set to capture within-task uncertainty. Tighter bounds are then developed for the two classes via novel individual task MI (ITMI) bounds. Applications of the derived bounds are finally discussed, including a broad class of noisy iterative algorithms for meta-learning.

scholarly journals Automated Detection and Classification of Desmoplastic Reaction at the Colorectal Tumour Front Using Deep Learning

Cancers ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 1615
Author(s):  
Ines P. Nearchou ◽  
Hideki Ueno ◽  
Yoshiki Kajiwara ◽  
Kate Lillard ◽  
Satsuki Mochizuki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Prognostic Significance ◽  
Stage Ii ◽  
Full Cohort ◽  
Test Set ◽  
Colorectal Tumour ◽  
Desmoplastic Reaction ◽  
Deep Learning Algorithm

The categorisation of desmoplastic reaction (DR) present at the colorectal cancer (CRC) invasive front into mature, intermediate or immature type has been previously shown to have high prognostic significance. However, the lack of an objective and reproducible assessment methodology for the assessment of DR has been a major hurdle to its clinical translation. In this study, a deep learning algorithm was trained to automatically classify immature DR on haematoxylin and eosin digitised slides of stage II and III CRC cases (n = 41). When assessing the classifier’s performance on a test set of patient samples (n = 40), a Dice score of 0.87 for the segmentation of myxoid stroma was reported. The classifier was then applied to the full cohort of 528 stage II and III CRC cases, which was then divided into a training (n = 396) and a test set (n = 132). Automatically classed DR was shown to have superior prognostic significance over the manually classed DR in both the training and test cohorts. The findings demonstrated that deep learning algorithms could be applied to assist pathologists in the detection and classification of DR in CRC in an objective, standardised and reproducible manner.

scholarly journals A study of Grammar Analysis in English Teaching With Deep Learning Algorithm

2020 ◽  
Vol 15 (18) ◽  
pp. 20
Author(s):  
Gang Zhang
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Experimental Results ◽  
P Value ◽  
Analysis Method ◽  
English Teaching ◽  
Deep Learning Algorithm ◽  
R Value ◽  
Grammar Analysis ◽  
Teaching Grammar

In English teaching, grammar is a very important part. Based on the seq2seq model, a grammar analysis method combining the attention mechanism, word embedding and CNN seq2seq was designed using the deep learning algorithm, then the algorithm training was completed on NUCLE, and it was tested on CoNIL-2014. The experimental results showed that of seq2seq+attention improved 33.43% compared to the basic seq2seq; in the comparison between the method proposed in this study and CAMB, the P value of the former was 59.33% larger than that of CAMB, the R value was 8.9% larger, and the value of was 42.91% larger. Finally, in the analysis of the actual students' grammar homework, the proposed method also showed a good performance. The experimental results show that the method designed in this study is effective in grammar analysis and can be applied and popularized in actual English teaching.

Ensemble-based deep meta learning for medical image segmentation

2021 ◽  
pp. 1-7
Author(s):  
Usman Ahmed ◽  
Jerry Chun-Wei Lin ◽  
Gautam Srivastava
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Medical Image Segmentation ◽  
The State ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Meta Learning ◽  
Small Set

Deep learning methods have led to a state of the art medical applications, such as image classification and segmentation. The data-driven deep learning application can help stakeholders to collaborate. However, limited labelled data set limits the deep learning algorithm to generalize for one domain into another. To handle the problem, meta-learning helps to learn from a small set of data. We proposed a meta learning-based image segmentation model that combines the learning of the state-of-the-art model and then used it to achieve domain adoption and high accuracy. Also, we proposed a prepossessing algorithm to increase the usability of the segments part and remove noise from the new test image. The proposed model can achieve 0.94 precision and 0.92 recall. The ability to increase 3.3% among the state-of-the-art algorithms.

Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs

GEOMATICA ◽  
2021 ◽  
pp. 1-23
Author(s):  
Roholah Yazdan ◽  
Masood Varshosaz ◽  
Saied Pirasteh ◽  
Fabio Remondino
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Automatic Segmentation ◽  
Geometric Constraints ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Traffic Signs ◽  
Deep Learning Algorithm ◽  
Comparison Results

Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided.

GENERATING BALANCED LEARNING AND TEST SETS FOR FUNCTION APPROXIMATION PROBLEMS

2011 ◽  
Vol 21 (03) ◽  
pp. 247-263 ◽  
Author(s):  
J. P. FLORIDO ◽  
H. POMARES ◽  
I. ROJAS
Keyword(s):  
Function Approximation ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Original Data ◽  
Input Output ◽  
Output Data ◽  
Data Set ◽  
Out Of Sample ◽  
Test Sets ◽  
Approximation Problems

In function approximation problems, one of the most common ways to evaluate a learning algorithm consists in partitioning the original data set (input/output data) into two sets: learning, used for building models, and test, applied for genuine out-of-sample evaluation. When the partition into learning and test sets does not take into account the variability and geometry of the original data, it might lead to non-balanced and unrepresentative learning and test sets and, thus, to wrong conclusions in the accuracy of the learning algorithm. How the partitioning is made is therefore a key issue and becomes more important when the data set is small due to the need of reducing the pessimistic effects caused by the removal of instances from the original data set. Thus, in this work, we propose a deterministic data mining approach for a distribution of a data set (input/output data) into two representative and balanced sets of roughly equal size taking the variability of the data set into consideration with the purpose of allowing both a fair evaluation of learning's accuracy and to make reproducible machine learning experiments usually based on random distributions. The sets are generated using a combination of a clustering procedure, especially suited for function approximation problems, and a distribution algorithm which distributes the data set into two sets within each cluster based on a nearest-neighbor approach. In the experiments section, the performance of the proposed methodology is reported in a variety of situations through an ANOVA-based statistical study of the results.

scholarly journals Construction of a far-ultraviolet all-sky map from an incomplete survey: application of a deep learning algorithm

2021 ◽  
Vol 502 (3) ◽  
pp. 3200-3209
Author(s):  
Young-Soo Jo ◽  
Yeon-Ju Choi ◽  
Min-Gi Kim ◽  
Chang-Ho Woo ◽  
Kyoung-Wook Min ◽  
...  
Keyword(s):  
Neural Network ◽  
Galaxy Evolution ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Activation Function ◽  
Dense Layer ◽  
Optical Parameters ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Far Ultraviolet

ABSTRACT We constructed a far-ultraviolet (FUV) all-sky map based on observations from the Far Ultraviolet Imaging Spectrograph (FIMS) aboard the Korean microsatellite Science and Technology SATellite-1. For the ${\sim}20{{\ \rm per\ cent}}$ of the sky not covered by FIMS observations, predictions from a deep artificial neural network were used. Seven data sets were chosen for input parameters, including five all-sky maps of H α, E(B − V), N(H i), and two X-ray bands, with Galactic longitudes and latitudes. 70 ${{\ \rm per\ cent}}$ of the pixels of the observed FIMS data set were randomly selected for training as target parameters and the remaining 30 ${{\ \rm per\ cent}}$ were used for validation. A simple four-layer neural network architecture, which consisted of three convolution layers and a dense layer at the end, was adopted, with an individual activation function for each convolution layer; each convolution layer was followed by a dropout layer. The predicted FUV intensities exhibited good agreement with Galaxy Evolution Explorer observations made in a similar FUV wavelength band for high Galactic latitudes. As a sample application of the constructed map, a dust scattering simulation was conducted with model optical parameters and a Galactic dust model for a region that included observed and predicted pixels. Overall, FUV intensities in the observed and predicted regions were reproduced well.

scholarly journals Detection of Pine Cones in Natural Environment Using Improved YOLOv4 Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ze Luo ◽  
Yizhuo Zhang ◽  
Keqi Wang ◽  
Liping Sun
Keyword(s):  
Natural Environment ◽  
Network Performance ◽  
Feature Fusion ◽  
Learning Algorithm ◽  
Image Data ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Pine Cone ◽  
Output Dimension ◽  
Pine Cones

Achieving the rapid and accurate detection of pine cones in the natural environment is essential for yield estimation and automatic picking. However, the complex background and tiny target pose a significant challenge to pine cone detection. This paper proposes a pine cone detection method using the improved You Only Look Once (YOLO) version 4 algorithm to overcome these challenges. First, the original pine cone image data come from a natural pine forest. Crawler technology is utilized to collect more pine cone images from the Internet to expand the data set. Second, the densely connected convolution network (DenseNet) structure is introduced in YOLOv4 to improve feature reuse and network performance. In addition, the backbone network is pruned to reduce the computational complexity and keep the output dimension unchanged. Finally, for the problem of feature fusion at different scales, an improved neck network is designed using the scale-equalizing pyramid convolution (SEPC). The experimental results show that the improved YOLOv4 model is better than the original YOLOv4 network; the average values of precision, recall, and AP reach 96.1%, 90.1%, and 95.8%; the calculation amount of the model is reduced by 21.2%; the detection speed is fast enough to meet the real-time requirements. This research could serve as a technical reference for estimating yields and automating the picking of pine cones.

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