An Effective Denoising and Enhancement Strategy for Medical Image Using Rl-Gl-Caputo Method

Ganga, M.; Janakiraman, N.; Sivaraman, Arun Kumar; Vincent, Rajiv; Muralidhar, A.; Ravindran, P.

doi:10.3233/apc210074

Cited by 15 publications

(10 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig. 7 shows the ROC curve and useful for assigning the best cutoffs to the classification [49]. These two parameters are mathematically expressed as sensitivity = TP/(FN + TP) and specificity = TN/(FP + TN).…”

Section: Experiments Resultsmentioning

confidence: 99%

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

Arunachalam¹,

Janakiraman²,

Rashid³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

In this research work, we proposed a medical image analysis framework with two separate releases whether or not Synovial Sarcoma (SS) is the cell structure for cancer. Within this framework the histopathology images are decomposed into a third-level sub-band using a two-dimensional Discrete Wavelet Transform. Subsequently, the structure features (SFs) such as Principal Components Analysis (PCA), Independent Components Analysis (ICA) and Linear Discriminant Analysis (LDA) were extracted from this subband image representation with the distribution of wavelet coefficients. These SFs are used as inputs of the Support Vector Machine (SVM) classifier. Also, classification of PCA + SVM, ICA + SVM, and LDA + SVM with Radial Basis Function (RBF) kernel the efficiency of the process is differentiated and compared with the best classification results. Furthermore, data collected on the internet from various histopathological centres via the Internet of Things (IoT) are stored and shared on blockchain technology across a wide range of image distribution across secure data IoT devices. Due to this, the minimum and maximum values of the kernel parameter are adjusted and updated periodically for the purpose of industrial application in device calibration. Consequently, these resolutions are presented with an excellent example of a technique for training and testing the cancer cell structure prognosis methods in spindle shaped cell (SSC) histopathological imaging databases. The performance characteristics of cross-validation are evaluated with the help of the receiver operating characteristics (ROC) curve, and significant differences in classification performance between the techniques are analyzed. The combination of LDA + SVM technique has been proven to be essential for intelligent SS cancer detection in the future, and it offers excellent classification accuracy, sensitivity, specificity.

show abstract

Section: Experiments Resultsmentioning

confidence: 99%

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

Arunachalam¹,

Janakiraman²,

Rashid³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Homomorphic encryption was used in [23] for accomplishing the security of large information put away in the fog. In this work, diverse fog notes were empowered and isolated for performing computational investigation of various pieces of the information.…”

Section: Literature Surveymentioning

confidence: 99%

Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

Priyadarsini¹,

Sivaraman²,

Quadir³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

With the rapid evolution of Internet technology, fog computing has taken a major role in managing large amounts of data. The major concerns in this domain are security and privacy. Therefore, attaining a reliable level of confidentiality in the fog computing environment is a pivotal task. Among different types of data stored in the fog, the 3D point and mesh fog data are increasingly popular in recent days, due to the growth of 3D modelling and 3D printing technologies. Hence, in this research, we propose a novel scheme for preserving the privacy of 3D point and mesh fog data. Chaotic Cat mapbased data encryption is a recently trending research area due to its unique properties like pseudo-randomness, deterministic nature, sensitivity to initial conditions, ergodicity, etc. To boost encryption efficiency significantly, in this work, we propose a novel Chaotic Cat map. The sequence generated by this map is used to transform the coordinates of the fog data. The improved range of the proposed map is depicted using bifurcation analysis. The quality of the proposed Chaotic Cat map is also analyzed using metrics like Lyapunov exponent and approximate entropy. We also demonstrate the performance of the proposed encryption framework using attacks like brute-force attack and statistical attack. The experimental results clearly depict that the proposed framework produces the best results compared to the previous works in the literature.

show abstract

“…In Type-2 decision-making, we propose a general framework with a figure. Readers can look in for recent papers [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Dhanalakshmi¹,

Samanta²,

Sivaraman³

et al. 2023

Computer Systems Science and Engineering

Self Cite

View full text Add to dashboard Cite

Fuzzy sets have undergone several expansions and generalisations in the literature, including Atanasov's intuitionistic fuzzy sets, type 2 fuzzy sets, and fuzzy multisets, to name a few. They can be regarded as fuzzy multisets from a formal standpoint; nevertheless, their interpretation differs from the two other approaches to fuzzy multisets that are currently available. Hesitating fuzzy sets (HFS) are very useful if consultants have hesitation in dealing with group decision-making problems between several possible memberships. However, these possible memberships can be not only crisp values in [0,1], but also interval values during a practical evaluation process. Hesitant bipolar valued fuzzy set (HBVFS) is a generalization of HFS. This paper aims to introduce a general framework of multi-attribute group decision-making using social network. We propose two types of decision-making processes: Type-1 decision-making process and Type-2 decision-making process. In the Type-1 decision-making process, the experts' original opinion is proces for the final ranking of alternatives. In Type-2 decision making processs, there are two major aspects we consider. First, consistency tests and checking of consensus models are given for detecting that the judgments are logically rational. Otherwise, the framework demands (partial) decision-makers to review their assessments. Second, the coherence and consensus of several HBVFSs are established for final ranking of alternatives. The proposed framework is clarified by an example of software packages selection of a university.

show abstract

An Effective Denoising and Enhancement Strategy for Medical Image Using Rl-Gl-Caputo Method

Cited by 15 publications

References 15 publications

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

Effective Classification of Synovial Sarcoma Cancer Using Structure Features and Support Vectors

Securing 3D Point and Mesh Fog Data Using Novel Chaotic Cat Map

Multi-attribute Group Decision-making Based on Hesitant Bipolar-valued Fuzzy Information and Social Network

Contact Info

Product

Resources

About