Convolutional Neural Network based Automated Detection of Mycobacterium Bacillus from Sputum Images

Swetha, K; Sankaragomathi, B.; Thangamalar, J. Babitha

doi:10.1109/icict48043.2020.9112501

Cited by 10 publications

(8 citation statements)

References 13 publications

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“…TB is the pathogenic bacterium that causes tuberculosis. Swetha et al (2020) proposes a framework for automatic and rapid classification of TB in sputum images. In this framework, the image is pre-processed by applying noise reduction and intensity modification, and then the segmentation is done by the Channel Area Thresholding (CAT).…”

Section: Classification Based On Cnnmentioning

confidence: 99%

Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer

et al. 2022

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The analysis of microorganisms is essential for making full use of different microorganisms. The conventional analysis methods are laborious and timeconsuming. Therefore, the automatic image analysis based on artificial neural networks is introduced to optimize it. However, the automatic microorganism image analysis faces many challenges, such as the requirement of a robust algorithm caused by various application occasions, insignificant features and easy under-segmentation caused by the image characteristic, and various analysis tasks. Therefore, we conduct this review to comprehensively discuss the characteristics of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are presented. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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Section: Classification Based On Cnnmentioning

confidence: 99%

Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer

et al. 2022

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“…Mycobacterium tuberculosis is the pathogenic bacterium that causes tuberculosis. In [134], the purpose is to use advanced image processing techniques to achieve automatic and rapid detection of tuberculosis in sputum images. The data used in this study is collected from an infected person.…”

Section: Datasetmentioning

confidence: 99%

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Zhang¹,

Li²,

Yin³

et al. 2021

Preprint

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Microorganisms are widely distributed in the human daily living environment. They play an essential role in environmental pollution control, disease prevention and treatment, and food and drug production. The identification, counting, and detection are the basic steps for making full use of different microorganisms. However, the conventional analysis methods are expensive, laborious, and time-consuming. To overcome these limitations, artificial neural networks are applied for microorganism image analysis. We conduct this review to understand the development process of microorganism image analysis based on artificial neural networks. In this review, the background and motivation are introduced first. Then, the development of artificial neural networks and representative networks are introduced. After that, the papers related to microorganism image analysis based on classical and deep neural networks are reviewed from the perspectives of different tasks. In the end, the methodology analysis and potential direction are discussed.

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“…The CNN method has been widely used by researchers to classify images [17]. The CNN algorithm is claimed to be the best method for solving the problem of acquiring an object [18]. There are several CNN architectures used in image classification, including ImageNet, GoogleNet, AlexNet, VGGNet, and ResNet 101.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN

Waluyo¹,

Isnanto²,

Rochim³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Mycobacterium tuberculosis is a pathogenic bacterium that causes respiratory tract disease in the lungs, namely tuberculosis (TB). The problem is to find out the bacterial colonies when the observation is still done manually using a microscope with a magnification of 1000 times. It took a long time and was tiring for the observer's eye. Based on this background, an automatic detection system for Mycobacterium tuberculosis was designed. Mycobacterium tuberculosis image data were obtained from the Semarang City Health Center. The dataset used is 220 sputum images, which are divided into 180 training data and 40 testing data. The method used in this research is a combination of Convolutional Neural Network (CNN) and K-Nearest Neighbor (KNN). CNN is used for image feature extraction. Furthermore, the results of the CNN feature extraction are classified using the KNN. The results of the accuracy of the combination of CNN-KNN and CNN were also compared. The stages of the process are color transformation, feature extraction, and data training with CNN, then classification with KNN. The results of the classification test between CNN and the CNN-KNN combination show that the CNN-KNN combination is better. The result of CNN-KNN accuracy is 92.5%, while CNN's accuracy is 90%.

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Convolutional Neural Network based Automated Detection of Mycobacterium Bacillus from Sputum Images

Cited by 10 publications

References 13 publications

Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer

Applications of artificial neural networks in microorganism image analysis: a comprehensive review from conventional multilayer perceptron to popular convolutional neural network and potential visual transformer

Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer

Comparison of Mycobacterium Tuberculosis Image Detection Accuracy Using CNN and Combination CNN-KNN

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