Classification of Skin cancer using deep learning, Convolutional Neural Networks - Opportunities and vulnerabilities- A systematic Review

Manne, Ravi; Kantheti, Snigdha; Kantheti, Sneha

doi:10.46501/ijmtst061118

Cited by 44 publications

(2 citation statements)

References 12 publications

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“…Introduced an anisotropic diffusion model dependent on LBM for picture division and showed the adequacy of the calculation in clinical pictures [55], [61][62][63][64][65]. Proposed a novel LBM method using the D2Q19 lattice arrangement model for the Segmentation of MR and clinical images, which is similar to anisotropic diffusion as shown in figure 2 [56], [66][67][68][69][70][71]. Unsigned Pressure Force (UPF) in light of regional attribute can adequately and effectively stop the contour at feeble obscured edges [57].…”

Section: Eai Endorsed Transactions On Pervasive Health and Technologymentioning

confidence: 99%

A Review of Medical Image Segmentation Algorithms

Ramesh¹,

Kumar²,

Swapna³

et al. 2021

EAI Endorsed Trans Perv Health Tech

140

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INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM. OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation. METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM). CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.

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Section: Eai Endorsed Transactions On Pervasive Health and Technologymentioning

confidence: 99%

A Review of Medical Image Segmentation Algorithms

Ramesh¹,

Kumar²,

Swapna³

et al. 2021

EAI Endorsed Trans Perv Health Tech

140

View full text Add to dashboard Cite

show abstract

“…(3) After treatment, the condition is stable and it is still necessary to monitor and evaluate the possible adverse reactions. Every three months in the first year, every six months after that (Manne et al, 2020).…”

Section: B Immune Checkpoint Inhibitormentioning

confidence: 99%

Impact and Control of Drug Therapy Guidelines for Tumor Patients During the Novel Coronavirus Pneumonia Epidemic

Ahmed¹,

Shariff²,

Ismail³

et al. 2021

Journal of Computer Science

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Since December 2019, many unexplained viral pneumonia cases have been found in Wuhan City, Hubei Province, China. It was later confirmed that the outbreak's causative agent was a new coronavirus. The virus was temporarily named "2019-new coronavirus" (2019-nCoV) by the World Health Organization (WHO). The diseases caused by 2019-nCoV were called by the National Health and Health Commission of China "New coronavirus pneumonia" (Novel Coronavirus Pneumonia, NCP) and was named "Coronavirus disease 2019" (COVID-19) by the WHO. The outbreak of NCP seriously affected the lives of the public. This article focuses on the group of cancer patients, comprehensively considers the social, medical resources and family issues to analyze the possible impact of the epidemic on cancer patients' drug treatment and health and makes recommendations for cancer patients' management.

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Automated rain fall prediction enabled by optimized convolutional neural network‐based feature formation with adaptive long short‐term memory framework

Ananthajothi¹,

Karthick

2022

Concurrency and Computation

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The main concept of this article is to plan for the intelligent rainfall prediction using the combination of deep learning models. The dataset is gathered from the standard publically available dataset concerning the Tamil Nadu state. The collected data is given to the feature extraction, in which few features such as; "minimum value, maximum value, mean, median, standard deviation, kurtosis, entropy, skewness, variance, and zero cross" are extracted. Additionally, the extracted features are applied to the optimal feature formation, in which optimized convolutional neural network (O-CNN) is employed for the final feature formation. Here, the activation function, count of pooling layer, and count of hidden neurons are tuned with the intention of minimizing the correlation between the selected features. Once the optimal features are selected with less correlation, adaptive long short-term memory (A-LSTM) is adopted for the prediction model. Here, the enhancement is concentrated on minimizing the function concerning the error through the optimization of the hidden neurons of A-LSTM. The improvement of both the deep learning models O-CNN and A-LSTM is performed by the improved sun flower optimization (I-SFO). The research results reveal superior performance to existing techniques that offer novel thinking in rainfall prediction area with optimal rate of prediction. K E Y W O R D Sadaptive long-short term memory, automated rainfall prediction, improved sun flower optimization, optimized convolutional neural network INTRODUCTIONThe rainfall 1 represents a climatic feature, which damages several human activities such as tourism, forestry, power generation, construction, and agricultural production, among others. 2 The prediction of rainfall 3 is needed because this variable contains much correlation with undesirable natural events like avalanches, mass movements, flooding, and landslides. The society is being damaged by these incidents over the years. 4,5 Hence, an accurate technique for the rainfall 6 prediction considers the mitigation and preventive measures for these natural calamities. 7 The precipitation probability is predicted in a specific region and the future forecasting is predicted by estimating the rainfall 8 amount in particular regions. 9 It considers the rainfall volume estimation, error in prediction, and accuracy of prediction together with the rainfall 10 probability in that particular region. The forecasters prepare it through, "collecting, analyzing, verifying, modeling, simulating and doing research" on distinct parameters present. 11 Few constraints consist of relative humidity, and average mean monthly temperature (maximum and minimum). 12 The intensity

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Classification of Skin cancer using deep learning, Convolutional Neural Networks - Opportunities and vulnerabilities- A systematic Review

Cited by 44 publications

References 12 publications

A Review of Medical Image Segmentation Algorithms

A Review of Medical Image Segmentation Algorithms

Impact and Control of Drug Therapy Guidelines for Tumor Patients During the Novel Coronavirus Pneumonia Epidemic

Automated rain fall prediction enabled by optimized convolutional neural network‐based feature formation with adaptive long short‐term memory framework

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