scholarly journals STHarDNet: Swin Transformer with HarDNet for MRI Segmentation

2022 ◽  
Vol 12 (1) ◽  
pp. 468
Author(s):  
Yeonghyeon Gu ◽  
Zhegao Piao ◽  
Seong Joon Yoo
Keyword(s):  
Type Model ◽  
Image Size ◽  
Mri Segmentation ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Conventional Models ◽  
Transformer Model ◽  
Computation Speed ◽  
Segmentation Models ◽  
Better Than

In magnetic resonance imaging (MRI) segmentation, conventional approaches utilize U-Net models with encoder–decoder structures, segmentation models using vision transformers, or models that combine a vision transformer with an encoder–decoder model structure. However, conventional models have large sizes and slow computation speed and, in vision transformer models, the computation amount sharply increases with the image size. To overcome these problems, this paper proposes a model that combines Swin transformer blocks and a lightweight U-Net type model that has an HarDNet blocks-based encoder–decoder structure. To maintain the features of the hierarchical transformer and shifted-windows approach of the Swin transformer model, the Swin transformer is used in the first skip connection layer of the encoder instead of in the encoder–decoder bottleneck. The proposed model, called STHarDNet, was evaluated by separating the anatomical tracings of lesions after stroke (ATLAS) dataset, which comprises 229 T1-weighted MRI images, into training and validation datasets. It achieved Dice, IoU, precision, and recall values of 0.5547, 0.4185, 0.6764, and 0.5286, respectively, which are better than those of the state-of-the-art models U-Net, SegNet, PSPNet, FCHarDNet, TransHarDNet, Swin Transformer, Swin UNet, X-Net, and D-UNet. Thus, STHarDNet improves the accuracy and speed of MRI image-based stroke diagnosis.

PENGGUNAAN MODEL KOOPERATIF TIPE TEAM GAMES TOURNAMENT UNTUK MENINGKATKAN KEMAMPUAN SISWA SEKOLAH DASAR

2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Muhammad Noor
Keyword(s):  
Problem Solving ◽  
Sampling Technique ◽  
Control Group ◽  
Control Group Design ◽  
Team Games ◽  
Conventional Models ◽  
Grade Material ◽  
Normalized Gain ◽  
Cooperative Models ◽  
Better Than

The purpose of this study was to obtain empirical evidence about the use of cooperative models of Team Games Tournament to increase the ability of students on solving problems with the summation material fractions. To achieve these objectives, the research carried out in the form of an experiment by comparing the problem solving ability of students to the material sum of fractions through the use cooperative model of TGT and students who received conventional learning. The design is a pretest-posttest control group design. The sampling technique used is purposive sampling technique. The instrument used is to use tests that pretest and posttest. The data were analyzed quantitatively for the results of the pretest, posttest, and normalized gain value. Based on data analysis in this study we concluded that there are differences in problem solving ability of students to the material sum of fractions through the use of cooperative models of Team Games Tournament with students who studied with conventional models, and improved problem solving abilities of students in the material that follows the fractional summation cooperative learning of TGT better than students who take the conventional learning model. Therefore, the ability of solving problems of students at grade material fractions summation cooperative modeled of TGT has increased quite good.

An Unequal-Sized Unidirectional Loop Layout Design Problem Considering Empty Vehicle Trip

2010 ◽  
Vol 37-38 ◽  
pp. 116-121
Author(s):  
Yu Lan Li ◽  
Bo Li ◽  
Su Jun Luo
Keyword(s):  
Material Handling ◽  
Design Principle ◽  
Facility Layout ◽  
Original Model ◽  
Layout Problem ◽  
Proposed Model ◽  
Loop Layout ◽  
Material Handling Costs ◽  
Loop Layout Problem ◽  
Better Than

In the facility layout decisions, the previous general design principle is to minimize material handling costs, and the objective of these old models only considers the costs of loaded trip, without regard to empty vehicle trip costs, which do not meet the actual demand. In this paper, the unequal-sized unidirectional loop layout problem is analyzed, and the model of facility layout is improved. The objective of the new model is to minimize the total loaded and empty vehicle trip costs. To solve this model, a heuristic algorithm based on partheno-genetic algorithms is designed. Finally, an unequal-sized unidirectional loop layout problem including 12 devices is simulated. Comparison shows that the result obtained using the proposed model is 20.4% better than that obtained using the original model.

The Effectiveness of the Cooperative Problem-Based Learning Model in Learning Statics in Vocational Education

2021 ◽  
Vol 12 (3) ◽  
pp. 3020-3027
Author(s):  
Juniman Silalahi Et.al
Keyword(s):  
Vocational Education ◽  
Learning Outcomes ◽  
Research Method ◽  
Problem Based Learning ◽  
Student Learning Outcomes ◽  
Simple Random Sample ◽  
Control Group ◽  
Conventional Models ◽  
Experimental Group ◽  
Better Than

This research aimed to determine the effectiveness of the Cooperative Problem-Based Learning (CPBL) Model in Learning Statics. The experimental class's research method was experimental, in which the experimental class was applied with the CPBL model, and the control class was applied with conventional models. A simple random sample carried out sampling for the experimental group and the control group. The instrument used was the learning outcomes test. The findings show that the experimental group's student learning outcomes are better than those of the control group. Thus, there is an increase in learning outcomes, and student effective results on the CPBL model in statics learning are in a very good category. It is concluded that the application of the CPBL model is more effective than conventional learning.

scholarly journals Unresectable Carcinoma Pancreas : Efficacy of Imaging in Diagnosing and Labeling them as ‘Unresectable’

2013 ◽  
Vol 12 (3) ◽  
pp. 23-26 ◽  
Author(s):  
Md Abdullah Al Farooq ◽  
MA Mushfiqur Rahman ◽  
Tania Tajreen ◽  
Eqramur Rahman ◽  
Md Minhajuddin Sajid ◽  
...  
Keyword(s):  
Ct Scan ◽  
Pancreatic Carcinoma ◽  
Imaging Modality ◽  
Preoperative Imaging ◽  
Mri Imaging ◽  
Medical College ◽  
Magnetic Resonance Imaging Mri ◽  
Carcinoma Pancreas ◽  
Operative Findings ◽  
Better Than

Background: Carcinoma pancreas is being diagnosed increasingly with the help of conventional imaging like ultrasonography (USG), computerized tomography (CT) scan and magnetic resonance imaging (MRI).Imaging also gives the opportunity to assess resectability. In our country MRI and CT scan are not widely available and most of the pancreatic carcinoma is too advanced for curative surgical resection when diagnosed. These are unresectable carcinoma pancreas (UCP). Objectives: To evaluate the efficacy of imaging in diagnosing carcinoma pancreas and to assess resectability after comparing them with peroperative findings. Methods: This retrospective study was carried out in the department of Hepato-Biliary-Pancreatic Surgery in Bangladesh Institute for Research and Rehabilitation in Diabetic Endocrine and Metabolic disorders (BIRDEM) hospital, Dhaka, Bangladesh from July 2004 to June 2006 (2 years). After laparotomy findings and histopathological confirmation 50 patients were labeled as UCP. Among 50 patients male were 28 & female patients were 22. Imaging modalities used before surgery was assessed and compared with per operative findings. USG were done in all patients and CTscan in 45 patients. MRI was done in 08 patients suspected clinically as pancreatic carcinoma where USG /CT scan had failed to reach a conclusion. Findings of the various imaging studies regarding diagnosis and unresectability were compared with per operative findings. Results: USG was able to diagnose 42 (84%) pancreatic carcinoma patients with unresectibility in 29 (69%). Forty five patients (90%) were diagnosed by CT scan and could label 38 (84.44%) as unresectable. MRI was 100% accurate to diagnose and label the entire 08 patient as unresectable carcinoma pancreas. Cumulative multimodal preoperative imaging was 91.33% accurate in diagnosing carcinoma pancreas and could tell the features of unresectibility in 73.59% patients. Conclusion: CT scan should be the primary imaging modality for diagnosing pancreatic carcinoma and its resectability. MRI is very promising for diagnosing and assessing UCP. Multimodal imaging is better than single imaging. Chattagram Maa-O-Shishu Hospital Medical College Journal Volume 12, Issue 3, September 2013: 23-26

scholarly journals The coalescent process in models with selection, recombination and geographic subdivision

1991 ◽  
Vol 57 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Norman Kaplan ◽  
Richard R. Hudson ◽  
Masaru Iizuka
Keyword(s):  
Genetic Variation ◽  
Population Genetic ◽  
Genetic Model ◽  
Sequence Data ◽  
Balancing Selection ◽  
Similar Model ◽  
Proposed Model ◽  
Coalescent Approach ◽  
Neutral Mutations ◽  
Better Than

SummaryA population genetic model with a single locus at which balancing selection acts and many linked loci at which neutral mutations can occur is analysed using the coalescent approach. The model incorporates geographic subdivision with migration, as well as mutation, recombination, and genetic drift of neutral variation. It is found that geographic subdivision can affect genetic variation even with high rates of migration, providing that selection is strong enough to maintain different allele frequencies at the selected locus. Published sequence data from the alcohol dehydrogenase locus of Drosophila melanogaster are found to fit the proposed model slightly better than a similar model without subdivision.

scholarly journals Both Freud and Hoffman are Right: Anxious-Aggressive and Empathic Dimensions of Guilt

2008 ◽  
Vol 11 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Itziar Etxebarria ◽  
Pedro Apodaca
Keyword(s):  
Young Adults ◽  
Subjective Experience ◽  
Negative Emotions ◽  
The Other ◽  
Point Scale ◽  
Nested Models ◽  
Proposed Model ◽  
Better Than

The purpose of the study was to confirm a model which proposed two basic dimensions in the subjective experience of guilt, one anxious-aggressive and the other empathic, as well as another dimension associated but not intrinsic to it, namely, the associated negative emotions dimension. Participants were 360 adolescents, young adults and adults of both sexes. They were asked to relate one of the situations that most frequently caused them to experience feelings of guilt and to specify its intensity and that of 9 other emotions that they may have experienced, to a greater or lesser extent, at the same time on a 7-point scale. The proposed model was shown to adequately fit the data and to be better than other alternative nested models. This result supports the views of both Freud and Hoffman regarding the nature of guilt, contradictory only at a first glance.

scholarly journals Segmentation and Classification of Breast Cancer Tumour

2018 ◽  
Vol 2 (1) ◽  
pp. 14-18
Author(s):  
Gokalp Cinarer ◽  
Bulent Gursel Emiroglu ◽  
Ahmet Hasim Yurttakal
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Wavelet Approximation ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation  Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

scholarly journals Order-Dependent Event Models for Agent Interactions

2020 ◽  
Author(s):  
Debarun Bhattacharjya ◽  
Tian Gao ◽  
Dharmashankar Subramanian
Keyword(s):  
Arrival Rate ◽  
Event Data ◽  
Political Event ◽  
Instantaneous Rate ◽  
Agent Interactions ◽  
Occurrence Data ◽  
Proposed Model ◽  
Event Models ◽  
Better Than ◽  
Event Occurrence

In multivariate event data, the instantaneous rate of an event's occurrence may be sensitive to the temporal sequence in which other influencing events have occurred in the history. For example, an agent’s actions are typically driven by preceding actions taken by the agent as well as those of other relevant agents in some order. We introduce a novel statistical/causal model for capturing such an order-sensitive historical dependence, where an event’s arrival rate is determined by the order in which its underlying causal events have occurred in the recent past. We propose an algorithm to discover these causal events and learn the most influential orders using time-stamped event occurrence data. We show that the proposed model fits various event datasets involving single as well as multiple agents better than baseline models. We also illustrate potentially useful insights from our proposed model for an analyst during the discovery process through analysis on a real-world political event dataset.

scholarly journals Classification & Feature extraction of Brain tumor from MRI Images using Modified ANN Approach

2021 ◽  
Vol 9 (2) ◽  
pp. 10-15
Author(s):  
Harendra Singh ◽  
Roop Singh Solanki
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Classification Feature ◽  
Proposed Model ◽  
Magnetic Resonance Imaging Mri ◽  
Artificial Neural Network Ann ◽  
High Degree

In this research paper, a new modified approach is proposed for brain tumor classification as well as feature extraction from Magnetic Resonance Imaging (MRI) after pre-processing of the images. The discrete wavelet transformation (DWT) technique is used for feature extraction from MRI images and Artificial Neural Network (ANN) is used for the classification of the type of tumor according to extracted features. Mean, Standard deviation, Variance, Entropy, Skewness, Homogeneity, Contrast, Correlation are the main features used to classify the type of tumor. The proposed model can give a better result in comparison with other available techniques in less computational time as well as a high degree of accuracy. The training and testing accuracies of the proposed model are 100% and 98.20% with a 98.70 % degree of precision respectively.

scholarly journals Improvements on Learning Kernel Extended Dictionary for Face Recognition

2020 ◽  
Vol 34 (4) ◽  
pp. 387-394
Author(s):  
Soodabeh Amanzadeh ◽  
Yahya Forghani ◽  
Javad Mahdavi Chabok
Keyword(s):  
Sparse Representation ◽  
Face Image ◽  
Collaborative Representation ◽  
Training Images ◽  
Input Face ◽  
Proposed Model ◽  
Eigen Values ◽  
New Type ◽  
The Mean ◽  
Better Than

Kernel extended dictionary learning model (KED) is a new type of Sparse Representation for Classification (SRC), which represents the input face image as a linear combination of dictionary set and extended dictionary set to determine the input face image class label. Extended dictionary is created based on the differences between the occluded images and non-occluded training images. There are four defaults to make about KED: (1) Similar weights are assigned to the principle components of occlusion variations in KED model, while the principle components of the occlusion variations have different weights, which are proportional to the principle components Eigen-values. (2) Reconstruction of an occluded image is not possible by combining only non-occluded images and the principle components (or the directions) of occlusion variations, but it requires the mean of occlusion variations. (3) The importance and capability of main dictionary and extended dictionary in reconstructing the input face image is not the same, necessarily. (4) KED Runtime is high. To address these problems or challenges, a novel mathematical model is proposed in this paper. In the proposed model, different weights are assigned to the principle components of occlusion variations; different weights are assigned to the main dictionary and extended dictionary; an occluded image is reconstructed by non-occluded images and the principle components of occlusion variations, and also the mean of occlusion variations; and collaborative representation is used instead of sparse representation to enhance the runtime. Experimental results on CAS-PEAL subsets showed that the runtime and accuracy of the proposed model is about 1% better than that of KED.

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