Deep learning based-classification of dementia in magnetic resonance imaging scans

The present study aimed to assess the influence of physiological exposure to erythrocyte indices; the morphometric parameters of the size of erythrocytes, as well as synergistic effects on the erythrocyte spectrum changes in various species being reared in different strip-wasteland conditions. We collected samples from 400 animals from the jugular veins and made 2000 blood smears, sample blood was taken by an expert veterinary doctor and transferred into an EDTA vacutainer. Blood smears were stained through the Pappenheim procedure and slides were photomicrographed under 100x objective. The parameters of morphometry of erythrocytes were largest in cattle after buffalo, sheep, while goats have the lowest one. The influence of age was significant p<0.05. The influence of sex was not significant in Cattle p<0.01, values of a parameter of erythrocytes were higher in young than in adults in sheep, goats, cattle, and buffalo. The size of erythrocytes in males was higher than females in sheep, goat, and buffalo but in cattle values of female erythrocytes were higher than in males. Further, gender, altitude, and age have a profound influence on the morphometric attributes of erythrocytes. It will be helpful in interpreting etiopathogenetic conditions in human beings and other animals.

show abstract

“…To better describe the results, we use Python for the graph, module name ". matplotlib" (Ucuzal et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

Morphometric analysis on erythrocytes of various livestock being reared in the Cholistan desert

Parveen

Lashari

Farooq

2023

Arq. Bras. Med. Vet. Zootec.

View full text Add to dashboard Cite

show abstract

“…Trough a large number of theories and experiments, it is proved that the convolutional neural network in deep learning plays an irreplaceable role in the feld of image classifcation. It can obtain image features through a large number of sample training, and the designed classifer is closely related to the extracted features [14]. Compared with traditional classifcation algorithms, convolutional neural networks can automatically learn the features of image data, and people do not need to spend a lot of experience manually extracting image features.…”

Section: Related Workmentioning

confidence: 99%

An Image Classification Method Based on Adaptive Attention Mechanism and Feature Extraction Network

Luo

2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The convolution neural network (CNN) not only has high fault tolerance but also has high computing capacity. The image classification performance of CNN has an important relationship with its network depth. The network depth is deeper, and the fitting ability of CNN is stronger. However, a further increase in the depth of CNN will not improve the accuracy of the network but will produce higher training errors, which will reduce the image classification performance of CNN. In order to solve the above problems, this paper proposes a feature extraction network, AA-ResNet with an adaptive attention mechanism. The residual module of the adaptive attention mechanism is embedded for image classification. It consists of a feature extraction network guided by the pattern, a generator trained in advance, and a complementary network. The feature extraction network guided by the pattern is used to extract different levels of features to describe different aspects of an image. The design of the model effectively uses the image information of the whole level and the local level, and the feature representation ability is enhanced. The whole model is trained as a loss function, which is about a multitask problem and has a specially designed classification, which helps to reduce overfitting and make the model focus on easily confused categories. The experimental results show that the method in this paper performs well in image classification for the relatively simple Cifar-10 dataset, the moderately difficult Caltech-101 dataset, and the Caltech-256 dataset with large differences in object size and location. The fitting speed and accuracy are high.

show abstract

“…Open-source software for deep learning classification of dementia based on magnetic resonance imaging scans. Keras (i.e., deep learning framework), which is the difference between possible dementia patients and healthy people, has been adopted to build a deep learning model [8]. Given the query image taken from a dataset of 443 images, the goal is to rank images based on similarity.…”

Section: Related Workmentioning

confidence: 99%

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

2020

jcr

View full text Add to dashboard Cite

Cancer detection has always been a challenge in the diagnosis and treatment plan for hematological diseases. Manual identification of cancers from microscopic images is necessary. It depends on other factors, including the lack of microscopic images as their knowledge and precise measurement of specific usual or cancer expert classifications. Automated identification of cancer cells from microscopic section images and helps to improve the problem mentioned above, based on biological interpretation. Detection of blood cancer cells by a patient's blood mark microscopic examination using different techniques of image processing. White blood cell cancer images and their essential three-stage there are an enhancement, segmentation, and classification. The stage of image processing to achieve more quality and accuracy in detecting blood cancers. Approaching the conventional segmentation and counting of blood cells is considered as an essential step that helps to extract features to diagnose diseases like leukemia. The image analysis will allow hematologist experts to perform faster and more accurately. In this work, the image recognition problem of White Blood Cells (WBC) cancer is investigated. Different types of white blood cells are classified using a Gaussian Feature Convolutional Visual Recognition (GFCVR).The most important features or segments of these blood cells are provided as input microscopic images to the neural network. The consequences of this analysis are mainly considering the identification of blood cancer affected region with feature extraction for specific white blood cell images. In this proposed approach pre-processing the image, classifies multi-level clustering to find the affected area in blood cells. In this proposed simulation, results in time complexity, accuracy, recall, perception, false classification ratio gives better results compare with other existing methods.

show abstract

Deep learning based-classification of dementia in magnetic resonance imaging scans

Cited by 12 publications

References 3 publications

Morphometric analysis on erythrocytes of various livestock being reared in the Cholistan desert

Morphometric analysis on erythrocytes of various livestock being reared in the Cholistan desert

An Image Classification Method Based on Adaptive Attention Mechanism and Feature Extraction Network

Detection of Leukemia in Microscopic White Blood Cell Images Using Gaussian Feature Convolutional Visual Recognition Algorithm

Contact Info

Product

Resources

About