Identifying the presence of bacteria on digital images by using asymmetric distribution with k-means clustering algorithm

Abstract

This paper is mainly aimed at the decomposition of image quality assessment study by using Three Parameter Logistic Mixture Model and k-means clustering (TPLMM-k). This method is mainly used for the analysis of various images which were related to several real time applications and for medical disease detection and diagnosis with the help of the digital images which were generated by digital microscopic camera. Several algorithms and distribution models had been developed and proposed for the segmentation of the images. Among several methods developed and proposed, the Gaussian Mixture Model (GMM) was one of the highly used models. One can say that almost the GMM was playing the key role in most of the image segmentation research works so far noticed in the literature. The main drawback with the distribution model was that this GMM model will be best fitted with a kind of data in the dataset. To overcome this problem, the TPLMM-k algorithm is proposed. The image decomposition process used in the proposed algorithm had been analyzed and its performance was analyzed with the help of various performance metrics like the Variance of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). According to the results, it is shown that the proposed algorithm achieves the better performance when compared with the previous results of the previous techniques. In addition, the decomposition of the images had been improved in the proposed algorithm.

Methodology for preparing a cosmetic sample for the development of Microorganism Detection System (SDM) software and artificial intelligence learning to recognize specific microbial species

“…An important issue is also the reduction of the analysis time, which is important for the manufacturer who wants quick confirmation of the purity of the obtained substrate, or the purity of the finished product. Deep machine learning and the analysis of microscopic images seem to be promising tools in microbiological analysis, which is confirmed by the presented research, as well as the results of other authors [20,[28][29][30][31]. Machine learning can significantly shorten the analysis time and reduce its costs while ensuring a high repeatability of results.…”

“…The thus obtained filter with stained microorganisms is dried and transferred to a microscope slide, takes microscope photos. The photos we made, were used for creating and learning of algorithms of SDM for microscopic image interpretation [19][20][21].…”

“…After trying different techniques for staining microorganisms, it was decided to use the Gram method with numerous modifications [20,25,26,27]. Gram staining is the most basic technique for bacterial identification, as well as a method for differentiating the bacterial species based on their chemical and physical properties into Gram-positive and Gram-negative groups.…”

“…By using deep learning, the computer system was taught to recognize and distinguish 33 species of bacteria Gram stained with an accuracy of recognition of 97.24 ±1.07% [28]. This procedure for staining was also proposed in 2018 by Smith et al [20] in a study using CNN for the automated analyses of cells from blood culture. The authors used deep neural networks and the Gram staining method, by which they obtained sensitivity and specificity of 98.4% and 75.0%, respectively, for Gram-positive cocci in chains and pairs, 93.2% and 97.2% for Gram-positive cocci in clusters, and 96.3% and 98.1% for Gram-negative rods.…”

“…The authors used deep neural networks and the Gram staining method, by which they obtained sensitivity and specificity of 98.4% and 75.0%, respectively, for Gram-positive cocci in chains and pairs, 93.2% and 97.2% for Gram-positive cocci in clusters, and 96.3% and 98.1% for Gram-negative rods. They also indicated that the analysis of the image of Gramstained microbes by artificial intelligence may have a unique application in clinical microbiology [20].…”