scholarly journals WBC-based segmentation and classification on microscopic images: a minor improvement

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1168
Author(s):  
Xin-Hui Lam ◽  
Kok-Why Ng ◽  
Yih-Jian Yoong ◽  
Seng-Beng Ng
Keyword(s):  
Transfer Learning ◽  
Lymphoblastic Leukemia ◽  
Research Work ◽  
Similarity Index ◽  
The Body ◽  
Classification Model ◽  
Dice Similarity Coefficient ◽  
Automated Method ◽  
Microscope Images ◽  
Learning Technique

Introduction White blood cells (WBCs) are immunity cells which fight against viruses and bacteria in the human body. Microscope images of captured WBCs for processing and analysis are important to interpret the body condition. At present, there is no robust automated method to segment and classify WBCs images with high accuracy. This paper aims to improve on WBCs image segmentation and classification method. Methods A triple thresholding method was proposed to segment the WBCs; meanwhile, a convolutional neural network (CNN)-based binary classification model that adopts transfer learning technique was proposed to detect and classify WBCs as a healthy or a malignant. The input dataset of this research work is the Acute Lymphoblastic Leukemia Image Database (ALL-IDB). The process first converts the captured microscope images into HSV format for obtaining the H component. Otsu thresholding is applied to segment the WBC area. A 13 × 13 kernel with two iterations was used to apply morphological opening on image to ameliorate output results. Collected cell masks were used to detect the contour of each cell on the original image. To classify WBCs into a healthy or a malignant category, characteristics and conditions of WBCs are to be examined. A transfer learning technique and pre-trained InceptionV3 model were employed to extract the features from the images for classification. Results The proposed WBCs segmentation method yields 90.45% accuracy, 83.81% of the structural similarity index, 76.25% of the dice similarity coefficient, and is computationally efficient. The accuracy of fine-tuned classifier model for training, validation and test sets are 93.27%, 92.31% and 96.15% respectively. The obtained results are high in accuracy and precision are over 96% and with lower loss value. Discussion Triple thresholding outperforms K-means clustering in segmenting smaller dataset. Pre-trained InceptionV3 model and transfer learning improve the flexibility and ability of classifier.

scholarly journals Design of Accurate Classification of COVID-19 Disease in X-Ray Images Using Deep Learning Approach

2021 ◽  
Vol 2 (2) ◽  
pp. 132-148
Author(s):  
Joy Iong-Zong Chen
Keyword(s):  
Transfer Learning ◽  
Research Work ◽  
Well Being ◽  
Classification Model ◽  
Classification Method ◽  
World Health ◽  
Detection Methods ◽  
X Rays ◽  
X Ray

COVID-19 appears to be having a devastating influence on world health and well-being. Moreover, the COVID-19 confirmed cases have recently increased to over 10 million worldwide. As the number of verified cases increase, it is more important to monitor and classify healthy and infected people in a timely and accurate manner. Many existing detection methods have failed to detect viral patterns. Henceforth, by using COVID-19 thoracic x-rays and the histogram-oriented gradients (HOG) feature extraction methodology; this research work has created an accurate classification method for performing a reliable detection of COVID-19 viral patterns. Further, the proposed classification model provides good results by leveraging accurate classification of COVID-19 disease based on the medical images. Besides, the performance of our proposed CNN classification method for medical imaging has been assessed based on different edge-based neural networks. Whenever there is an increasing number of a class in the training network, the accuracy of tertiary classification with CNN will be decreasing. Moreover, the analysis of 10 fold cross-validation with confusion metrics can also take place in our research work to detect various diseases caused due to lung infection such as Pneumonia corona virus-positive or negative. The proposed CNN model has been trained and tested with a public X-ray dataset, which is recently published for tertiary and normal classification purposes. For the instance transfer learning, the proposed model has achieved 85% accuracy of tertiary classification that includes normal, COVID-19 positive and Pneumonia. The proposed algorithm obtains good classification accuracy during binary classification procedure integrated with the transfer learning method.

Determination of Some Heavy Metals in Milk of Cow Grazing At Selected Areas, of Kaduna Metropolis

2017 ◽  
Vol 7 (7) ◽  
pp. 341 ◽  
Author(s):  
Mahmud Mohammed Imam ◽  
Zahra Muhammad ◽  
Amina Zakari
Keyword(s):  
Heavy Metals ◽  
Cadmium Toxicity ◽  
Research Work ◽  
The Body ◽  
Cow Milk ◽  
Scientific Model ◽  
Potential Health ◽  
Milk Samples ◽  
Lead And Cadmium ◽  
Digestion Technique

In this research work the concentration of zinc, copper, lead, chromium, cadmium, and nickel in cow milk samples obtained from four different grazing areas   (kakuri, kudendan, malali, kawo) of Kaduna metropolis. The samples were digested by wet digestion technique .The trace element were determined using bulk scientific model VPG 210 model  Atomic Absorption Spectrophotometer (AAS).. The concentration of the determined heavy metal were The result revealed that Cr,  Ni and Cd were not detected in milk samples from Kawo, Malali  and Kudendan whereas lead (Pb) is detected in all samples and found to be above  the stipulated limits of recommended dietary allowance (NRC,1989) given as 0.02mg/day. Cu and Zn are essential elements needed by the body for proper metabolism and as such their deficiency or excess is very dangerous for human health. However, they were found in all samples and are within the recommended limits while Cd (2.13 – 3.15 mg/kg) in milk samples from Kakuri was found to be above such limit (0.5mg/day). Cow milk samples analyzed for heavy metals in this research work pose a threat of lead and cadmium toxicity due to their exposure to direct sources of air, water and plants in these grazing areas, thereby, resulting to a potential health risk to the consumers.

scholarly journals Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Young-Gon Kim ◽  
Sungchul Kim ◽  
Cristina Eunbee Cho ◽  
In Hye Song ◽  
Hee Jin Lee ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Frozen Section ◽  
Medical Center ◽  
External Validation ◽  
Model Performance ◽  
Classification Model ◽  
Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

scholarly journals Research of Water Body Turbidity Classification Model for Aquiculture Based on Transfer Learning

2021 ◽  
Vol 1757 (1) ◽  
pp. 012004
Author(s):  
Jianhua Zheng ◽  
Gaolin Yang ◽  
Yanxuan Huang ◽  
Leian Liu ◽  
Guihuang Hong ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Water Body ◽  
Classification Model

Transfer learning based convolution neural net for authentication and classification of emotions from natural and stimulated speech signals

2021 ◽  
pp. 1-12
Author(s):  
Mukul Kumar ◽  
Nipun Katyal ◽  
Nersisson Ruban ◽  
Elena Lyakso ◽  
A. Mary Mekala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Transfer Learning ◽  
Research Work ◽  
Oral Communication ◽  
Neural Net ◽  
Emotion Classification ◽  
Style Transfer ◽  
Conventional Machine ◽  
Neural Network Algorithm

Over the years the need for differentiating various emotions from oral communication plays an important role in emotion based studies. There have been different algorithms to classify the kinds of emotion. Although there is no measure of fidelity of the emotion under consideration, which is primarily due to the reason that most of the readily available datasets that are annotated are produced by actors and not generated in real-world scenarios. Therefore, the predicted emotion lacks an important aspect called authenticity, which is whether an emotion is actual or stimulated. In this research work, we have developed a transfer learning and style transfer based hybrid convolutional neural network algorithm to classify the emotion as well as the fidelity of the emotion. The model is trained on features extracted from a dataset that contains stimulated as well as actual utterances. We have compared the developed algorithm with conventional machine learning and deep learning techniques by few metrics like accuracy, Precision, Recall and F1 score. The developed model performs much better than the conventional machine learning and deep learning models. The research aims to dive deeper into human emotion and make a model that understands it like humans do with precision, recall, F1 score values of 0.994, 0.996, 0.995 for speech authenticity and 0.992, 0.989, 0.99 for speech emotion classification respectively.

scholarly journals Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Entropy ◽  
2020 ◽  
Vol 23 (1) ◽  
pp. 56
Author(s):  
Haoyu Niu ◽  
Jiamin Wei ◽  
YangQuan Chen
Keyword(s):  
Research Work ◽  
Cauchy Distribution ◽  
Classification Model ◽  
Test Accuracy ◽  
Functional Link ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Improved Performance ◽  
Hidden Layer ◽  
Hidden Nodes

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.

Global food safety: determinants are Codex standards and WTO's SPS food safety regulations

2014 ◽  
Vol 11 (2) ◽  
pp. 176-191 ◽  
Author(s):  
Rajneesh Mahajan ◽  
Suresh Garg ◽  
P.B. Sharma
Keyword(s):  
Food Safety ◽  
Research Work ◽  
The Body ◽  
Successful Implementation ◽  
Processed Food ◽  
Food Sector ◽  
Content Type ◽  
Safety Regulations ◽  
Food Safety Regulations ◽  
Global Food

Purpose – The purpose of this paper is to investigate perspective in explaining how global food safety can be created through stringent implementation of Codex and World Trade Organization (WTOs) Sanitary and Phytosanitary food safety regulations and suggests the appropriate food safety system for India. Design/methodology/approach – The study has been deployed a survey questionnaire using a sample of Indian Processed food sector. In order to collect data 1,000 supply chain professional were contacted for seeking their consent to be part of the survey. Whereas total responses collected were 252 from Delhi and NCR, with response rate 25.2 percent. The data collected was empirical tested using descriptive statistics, correlation analysis, regression and ANOVA. Findings – The results and discussions indicate that all the global food safety norms laid down by WTO such as goods manufacturing practices, good hygienic practice, hazard analysis critical control point, has been developed to embody principles of safe food processing sector globally. India has also developed their food safety norms as per laid down principles by WTO. Originality/value – The present research work makes an important contribution to the body of literature on global food safety. The paper has important implications for the processed food sector since it tries to bring out practices which would help in successful implementation of global food safety standards. It is useful for academic food research as well as for processed food corporate.

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