scholarly journals Cancer Cell Detection in Bone Using ANN

2021 ◽  
pp. 208-217
Author(s):  
Mrs. R. Kavitha ◽  
Dr. N. Viswanathan
Keyword(s):  
Early Stage ◽  
Bone Cancer ◽  
Clinical Analysis ◽  
Cell Detection ◽  
Mr Images ◽  
Neural Network Algorithm ◽  
Time Required ◽  
Cancer Cell Detection ◽  
Artificial Neural Network Algorithm

A vigorous disease is bone cancer results in deaths of many people. The identification and classification system must be done at its early stage to diagnose. The early detection plays an important role to safe guard the patient from death. And also cancer categorization is one of the toughest tasks in clinical analysis. This paper deals with MR images of various patients used to identify the tumor and classify cancer using Artificial Neural Network algorithm. The proposed methodology uses filtering as preprocessing techniques followed by gray conversion and other image processing methods like edge detection, morphological operation, segmentation, feature extraction and classification are prepared for the identification of bone cancer. By this method time required is reduced for identification and classification of bone cancer.

scholarly journals Design of MEMS-based Microfludic Channel to Detect Cancer Cells in Blood

2020 ◽  
Vol 11 (1) ◽  
pp. 561-566
Author(s):  
Syed Shameem ◽  
RamaKrishna T V ◽  
Sahithi M ◽  
Rohitha B ◽  
Keerthana J ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Cancer Cell ◽  
Early Stage ◽  
Microfluidic Channel ◽  
Cell Detection ◽  
Malignant Growth ◽  
Simple Manner ◽  
Abnormal Cells ◽  
Mass Displacement ◽  
Cancer Cell Detection

Cancer refers to any of countless infections characterized by the development of abnormal cells that divide uncontrollably and can invade and destroy normal body tissue. Malignant growth frequently can spread all through your body. Cancer is the second driving reason for death on the planet. In this paper, we propose to found a H-cell to screen carcinogenic cells in a given sample of blood based on the principle of diffusion. This model incorporates the planning of a MEMS-based microfluidic channel to screen and recognize different cells depending on the size and various characteristics of the cells. Some of the methods which are implemented not efficient models for cancer cells detection in blood. The mass, displacement technique has been implemented in this investigation for cancer cell detection, with the help of this achieves the accuracy and better throughput. One cancer cell contains = 1.70371e-24 mass, such that with a weight of this formula, find out the total no of cells in the blood. This is the best method compared to existed methods. Using this count, the weight has been calculate early-stage cancer and treatment with a simple manner, CTCs in the blood is the un potential matter for health, H-cells have been measured with proposed weight and force technique such that in this investigation also calculate the healthy and cancer cells also. Finally, using this methodology achieves 93.58% accuracy, 0.00124 MSE. These are very good results compared to conventional methods.

scholarly journals Disease Identification in Maize Plant Leaf

2020 ◽  
Vol 9 (4) ◽  
pp. 1239-1244
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Plant Disease ◽  
Early Stage ◽  
Plant Leaves ◽  
Disease Identification ◽  
Neural Network Algorithm ◽  
The One ◽  
Automatic Technique

Agricultural productivity is that issue there on Indian Economy extremely depends. this is often the one altogether the reasons that malady detection in plants plays a really important role within the agriculture field, as having the malady in plants are quite natural. If correct care isn't taken during this space then it causes serious effects on plants and since of that various product quality, amount or productivity is affected. Detection of disease through some automatic technique is useful because it reduces an oversized work of watching in huge farms of crops, and at terribly early stage itself detects the symptoms of diseases means after they appear on plant leaves. This paper presents a neural network algorithm for image segmentation technique used for automatic detection still because the classification of plants and survey on completely different diseases classification techniques which will be used for plant disease detection. Image segmentation, that is a really important facet for malady detection in disease is completed by victimization genetic algorithm.

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

scholarly journals BioVerbNet: a large semantic-syntactic classification of verbs in biomedicine

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Olga Majewska ◽  
Charlotte Collins ◽  
Simon Baker ◽  
Jari Björne ◽  
Susan Windisch Brown ◽  
...  
Keyword(s):  
Natural Language ◽  
Language Processing ◽  
Model Performance ◽  
Representation Learning ◽  
Verb Classes ◽  
Expert Annotation ◽  
Biomedical Texts ◽  
Time Required ◽  
Verb Meaning

Abstract Background Recent advances in representation learning have enabled large strides in natural language understanding; However, verbal reasoning remains a challenge for state-of-the-art systems. External sources of structured, expert-curated verb-related knowledge have been shown to boost model performance in different Natural Language Processing (NLP) tasks where accurate handling of verb meaning and behaviour is critical. The costliness and time required for manual lexicon construction has been a major obstacle to porting the benefits of such resources to NLP in specialised domains, such as biomedicine. To address this issue, we combine a neural classification method with expert annotation to create BioVerbNet. This new resource comprises 693 verbs assigned to 22 top-level and 117 fine-grained semantic-syntactic verb classes. We make this resource available complete with semantic roles and VerbNet-style syntactic frames. Results We demonstrate the utility of the new resource in boosting model performance in document- and sentence-level classification in biomedicine. We apply an established retrofitting method to harness the verb class membership knowledge from BioVerbNet and transform a pretrained word embedding space by pulling together verbs belonging to the same semantic-syntactic class. The BioVerbNet knowledge-aware embeddings surpass the non-specialised baseline by a significant margin on both tasks. Conclusion This work introduces the first large, annotated semantic-syntactic classification of biomedical verbs, providing a detailed account of the annotation process, the key differences in verb behaviour between the general and biomedical domain, and the design choices made to accurately capture the meaning and properties of verbs used in biomedical texts. The demonstrated benefits of leveraging BioVerbNet in text classification suggest the resource could help systems better tackle challenging NLP tasks in biomedicine.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Cross-evaluation of social mining for classification of depressed online personas

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Alina Trifan ◽  
José Luis Oliveira
Keyword(s):  
Transfer Learning ◽  
Early Stage ◽  
Post Partum ◽  
Evaluation Study ◽  
Continuous Increase ◽  
Digital Phenotyping ◽  
Public Datasets ◽  
The Impact ◽  
Social Mining

Abstract With the continuous increase in the use of social networks, social mining is steadily becoming a powerful component of digital phenotyping. In this paper we explore social mining for the classification of self-diagnosed depressed users of Reddit as social network. We conduct a cross evaluation study based on two public datasets in order to understand the impact of transfer learning when the data source is virtually the same. We further complement these results with an experiment of transfer learning in post-partum depression classification, using a corpus we have collected for the matter. Our findings show that transfer learning in social mining might still be at an early stage in computational research and we thoroughly discuss its implications.

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