An automated system based on 2 d empirical mode decomposition and k-means clustering for classification of Plasmodium species in thin blood smear images

Manickavasagam, K; Sutha, S.; Krishnamurthy, Kamalanand

doi:10.1186/1471-2334-14-s3-p13

Cited by 21 publications

(6 citation statements)

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“…Seok et al [79] classified the genomic response between human inflammatory diseases. Manickavasagam et al [80] classified the Plasmodium species in thin blood smear images to control malarial infection disease. Antony and Ravi [81] classified mammographic images, reducing the false positive results that are generated by other methods.…”

Section: Applicationsmentioning

confidence: 99%

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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Human healthcare is one of the most important topics for society. It tries to find the correct effective and robust disease detection as soon as possible to patients receipt the appropriate cares. Because this detection is often a difficult task, it becomes necessary medicine field searches support from other fields such as statistics and computer science. These disciplines are facing the challenge of exploring new techniques, going beyond the traditional ones. The large number of techniques that are emerging makes it necessary to provide a comprehensive overview that avoids very particular aspects. To this end, we propose a systematic review dealing with the Machine Learning applied to the diagnosis of human diseases. This review focuses on modern techniques related to the development of Machine Learning applied to diagnosis of human diseases in the medical field, in order to discover interesting patterns, making non-trivial predictions and useful in decision-making. In this way, this work can help researchers to discover and, if necessary, determine the applicability of the machine learning techniques in their particular specialties. We provide some examples of the algorithms used in medicine, analysing some trends that are focused on the goal searched, the algorithm used, and the area of applications. We detail the advantages and disadvantages of each technique to help choose the most appropriate in each real-life situation, as several authors have reported. The authors searched Scopus, Journal Citation Reports (JCR), Google Scholar, and MedLine databases from the last decades (from 1980s approximately) up to the present, with English language restrictions, for studies according to the objectives mentioned above. Based on a protocol for data extraction defined and evaluated by all authors using PRISMA methodology, 141 papers were included in this advanced review.

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Section: Applicationsmentioning

confidence: 99%

Machine Learning Applied to Diagnosis of Human Diseases: A Systematic Review

et al. 2020

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“…In image processing discipline, image segmentation is a necessary procedure to extract significant information from gray or color images in many fields, such as remote sensing [1][2][3][4], medicine [5][6][7] and pattern recognition [8]. The obtained digital image usually defined as an array of real or complex numbers which can be represented by a finite number of bits, further segmentation to be done by using traditional or modern computational techniques.…”

Section: Introductionmentioning

confidence: 99%

“…These irregularities are caused by surrounding illumination, temperature, etc. [5,6,36].When there is a considerable noise level, it is necessary to preprocess the images using filters. When the noise is within an acceptable level, the filtering process can be avoided and the image can be directly processed using a chosen image processing methodology [36].…”

Section: Introductionmentioning

confidence: 99%

“…Table 13 presents a general overview of the results obtained with and without noisy images. In the literature, several statistical procedures, such as ANOVA [5,6], Kruskal-Wallis [50], etc., are available to compute statistical significant differences between methods. In the proposed work, the Wilcoxon's rank test [47,48] is taken for the process of image segmentation.…”

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confidence: 99%

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Multi-Level Thresholding with Fractional-Order Darwinian PSO and Tsallis Function

Pugalenthi¹,

Oliver²

2019

IJITEE

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A novel optimal multi-level thresholding is proposed using gray scale images for Fractional-order Darwinian Particle Swarm Optimization (FDPSO) and Tsallis function. The maximization of Tsallis entropy is chosen as the Objective Function (OF) which monitors FDPSO’s exploration until the search converges to an optimal solution. The proposed method is tested on six standard test images and compared with heuristic methods, such as Bat Algorithm (BA) and Firefly Algorithm (FA). The robustness of the proposed thresholding procedure was tested and validated on the considered image data set with Poisson Noise (PN) and Gaussian Noise (GN). The results obtained with this study verify that, FDPSO offers better image quality measures when compared with BA and FA algorithms. Wilcoxon’s test was performed by Mean Structural Similarity Index (MSSIM), and the results prove that image segmentation is clear even in noisy dataset based on the statistical significance of the FDPSO with respect to BA and FA.

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“…In image processing literature, variety of scheme is existing to perform the multi-thresholding process [32,33]. In this paper, analysis is presented for Kapur entropy function based on Bat algorithm.…”

Section: B Kapur's Entropymentioning

confidence: 99%