An Early Prediction of Tumor in Heart by Cardiac Masses Classification in Echocardiogram Images Using Robust Back Propagation Neural Network Classifier

Annamalai, Manikandan; Muthiah, Ponni Bala

doi:10.1590/1678-4324-2022210316

Cited by 25 publications

(3 citation statements)

References 19 publications

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“…Pneumonia" [4] "is contamination that inflames the air sacs in a single or each lung. The air sacs might also additionally fill with fluid or purulent material" [2][4], "inflicting cough with phlegm or pus, fever, chills, and problem in breathing." [2][4] "A style of organisms, which include bacteria, viruses and fungi, can purpose pneumonia."…”

Section: Introductionmentioning

confidence: 99%

“…The air sacs might also additionally fill with fluid or purulent material" [2][4], "inflicting cough with phlegm or pus, fever, chills, and problem in breathing." [2][4] "A style of organisms, which include bacteria, viruses and fungi, can purpose pneumonia." [2][4] "Pulmonary opacity is a nonspecific time period describing a place of elevated pulmonary attenuation as a result of an intraparenchymal process.…”

Section: Introductionmentioning

confidence: 99%

“…[2][4] "A style of organisms, which include bacteria, viruses and fungi, can purpose pneumonia." [2][4] "Pulmonary opacity is a nonspecific time period describing a place of elevated pulmonary attenuation as a result of an intraparenchymal process. Pulmonary opacification represents the end result of a lower within the ratio of fueloline to gentle tissue blood, lung parenchyma and stroma within the lung.…”

Section: Introductionmentioning

confidence: 99%

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A survey on Classification of Medical Images using Deep Learning

Manikandan¹

2021

JIPIRS

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The advancement of technology is creating ease in every field and one of the major fields that has benefitted from this is healthcare. Medical images which includes Multi resonance images, Computed tomography scans and many more have always been used significantly to get a better understanding of the disease, rather knowing the disease and blurred visions of the same can be a matter of concern since so much depends on the images as they are used for analysation. The Covid19 Pandemic hit the population and made things worse for humans and created havoc worldwide, and its early detection using the chest X-rays is significant and the use of technology in this can make it an even better process. So, to classify the chest x-ray images into covid19, viral pneumonia, lung opacity or normal can play a notable role in the area of research. For the indicated purpose, a pretrained model is used by using transfer learning, an efficentnetb4 is used for the same. For the evaluation part of the model, F1 score, sensitivity and precision are used. The method successfully classifies the chest x-ray images into four types, accompanied by an accuracy of 96.73% on the test set. Brain tumor has also been an area of major concern for a long time and various research has been done in the indicated field, but still a lot is left to be discovered. The classification of brain tumors not only helps in creating ease with treatment, but it also depicts that if a tumor is cancerous or not. For this purpose, MRI images are being used for classifying brain tumors into glioma, pituitary, no tumor, meningioma and for this also, a pretrained model is used by using transfer learning, efficentnetb4 is used for the same and for the evaluation part of the model, F1 score, sensitivity and precision are used. The method successfully classifies the brain mri images into four types with accuracy of 98.58% on the test set. While most of the existing techniques either use different methods for same modality or different methods for different modalities, this work uses same method efficientnetb4 for both different types of medical images x-ray as well as mri images.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A survey on Classification of Medical Images using Deep Learning

Manikandan¹

2021

JIPIRS

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Automated classification of emphysema using data augmentation and effective pixel location estimation with multi-scale residual network

Manikandan¹,

Maheswari²

2022

Neural Comput & Applic

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Brain tumor segmentation and classification using hybrid deep CNN with LuNetClassifier

Balamurugan¹,

Gnanamanoharan

2022

Neural Comput & Applic

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The early diagnosis of brain tumors is critical to enhancing patient survival and prospects. The magnetic resonance imaging (MRI) brain tumor images must be physically analyzed in this work. As a result, computerized approaches for more precise tumor diagnostics are required. However, evaluating shape, volume, borders, Tumor detection, size, segmentation, and classification remains challenging. This work proposes a hybrid Deep Convolutional Neural Network (DCNN) classifier using an enhanced LuNet classifier algorithm in this proposed work. The primary intention of this work is to determine the area of the tumor site and classify brain tumors as benign or malignant. Initially, we split the data using the extended LuNet algorithm. Grey-level co-occurrence matrix (GLCM) and VGG16 extracted the features, yielding 13 classification features. For pretreatment, the Laplacian of Gaussian filter (LOG) is used. Overall, the proposed approach tries to increase the performance of non-deep learning classifiers. Traditional classifiers are superior deep learning methods because they require fewer training data sets, have lower computing complexity, lower user costs, and are easier to use by people with less training experience. The proposed algorithm achieves a better accuracy rate of 99.7%. Compared to the existing algorithm, the proposed approach outperforms them.

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An Early Prediction of Tumor in Heart by Cardiac Masses Classification in Echocardiogram Images Using Robust Back Propagation Neural Network Classifier

Cited by 25 publications

References 19 publications

A survey on Classification of Medical Images using Deep Learning

A survey on Classification of Medical Images using Deep Learning

Automated classification of emphysema using data augmentation and effective pixel location estimation with multi-scale residual network

Brain tumor segmentation and classification using hybrid deep CNN with LuNetClassifier

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