Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Tchapga, Christian Tchito; Mih, Thomas Attia; Kouanou, Aurelle Tchagna; Fozin, T. Fonzin; Fogang, Platini Kuetche; Mezatio, Brice Anicet; Tchiotsop, Daniel

doi:10.1155/2021/9998819

Cited by 57 publications

(21 citation statements)

References 80 publications

(96 reference statements)

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“…Tchapga et al described how these algos can be adapted to big data structures applying the Spark interface. He presented a further work flow for taxonomy based on watched optimal algorithms, support vector mechanisms, and deep study [ 10 ]. Li et al proposed a basketball training algorithm utilizing big data and IoT.…”

Section: Related Workmentioning

confidence: 99%

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

Zhang

2022

Computational Intelligence and Neuroscience

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The basic idea of the mass of medical growth is to adhere to local market price thinking with a Chinese touch and follow the development policy of “quality first, efficiency first.” It insists on properly handling a series of important relationships betwixt socialism and market economy, the first to drive the rich later, the government and the market, equality and efficiency, short-term growth and long-term development, China and the international economy, ecology and growth of the region. Under the guidance of the qualitative thinking theory, it combines the strategic goals of China’s economic qualitative development and actively draws on the research results of other countries. It uses big data algorithms to focus on the impact of qualitative development on tax and income reduction in the real economy. It conducts research experiments on the impact of tax reduction and fee reduction based on big data algorithms on the top-notch growth of the real economy. Its experimental data show that: in 2018, the share of primary, tertiary, and primary sector in China’s dimensional economy top-notch growth coordination index was 7.2%, 40.7%, and 52.2%, respectively. Its contribution rate to economic growth was 4.2%, 36.1%, and 59.7%, respectively. From these data, it can be concluded that the top-notch growth of the real economy is getting better and better under the influence of tax reduction and fee reduction by big data algorithms.

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Section: Related Workmentioning

confidence: 99%

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

Zhang

2022

Computational Intelligence and Neuroscience

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“…By using our model, we can now perform a cheap COVID-19 test within less time. Furthermore, we can try to improve our model with some big data analysis techniques and tools used in biomedical engineering and presented in [ 52 – 54 ].…”

Section: Resultsmentioning

confidence: 99%

An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

Kouanou

Attia

Feudjio

et al. 2021

Journal of Healthcare Engineering

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Background and Objective. To mitigate the spread of the virus responsible for COVID-19, known as SARS-CoV-2, there is an urgent need for massive population testing. Due to the constant shortage of PCR (polymerase chain reaction) test reagents, which are the tests for COVID-19 by excellence, several medical centers have opted for immunological tests to look for the presence of antibodies produced against this virus. However, these tests have a high rate of false positives (positive but actually negative test results) and false negatives (negative but actually positive test results) and are therefore not always reliable. In this paper, we proposed a solution based on Data Analysis and Machine Learning to detect COVID-19 infections. Methods. Our analysis and machine learning algorithm is based on most cited two clinical datasets from the literature: one from San Raffaele Hospital Milan Italia and the other from Hospital Israelita Albert Einstein São Paulo Brasilia. The datasets were processed to select the best features that most influence the target, and it turned out that almost all of them are blood parameters. EDA (Exploratory Data Analysis) methods were applied to the datasets, and a comparative study of supervised machine learning models was done, after which the support vector machine (SVM) was selected as the one with the best performance. Results. SVM being the best performant is used as our proposed supervised machine learning algorithm. An accuracy of 99.29%, sensitivity of 92.79%, and specificity of 100% were obtained with the dataset from Kaggle (https://www.kaggle.com/einsteindata4u/covid19) after applying optimization to SVM. The same procedure and work were performed with the dataset taken from San Raffaele Hospital (https://zenodo.org/record/3886927#.YIluB5AzbMV). Once more, the SVM presented the best performance among other machine learning algorithms, and 92.86%, 93.55%, and 90.91% for accuracy, sensitivity, and specificity, respectively, were obtained. Conclusion. The obtained results, when compared with others from the literature based on these same datasets, are superior, leading us to conclude that our proposed solution is reliable for the COVID-19 diagnosis.

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“…Forecasting, recommendations, and estimations are important actions based on historical data for the training phase of the machine learning techniques. Computers behave like human beings by training these data with the help of historical data along with forecasted data [34,35].…”

Section: Support Vector Machine For Classification Of Diseased Betel ...mentioning

confidence: 99%

Agronomy with IoT Devices: The Smart Solution for Detection of Diseases of Betel Leaves

Sarkar¹,

Karforma²,

Bhattacharya³

et al. 2023

ujar

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The growth of the entire population in the entire world and scarcity of food crops are the most challenging concept nowadays and for solving these challenges, new mechanization like artificial intelligence, Internet of Things and the mobile internet are used for solving this above-mentioned real-life problem. From 2019 onwards, a current perspective regarding Intelligent Agronomy has been focused on. For this smart system, Internet of Thing (IoT) is the column pillar because sensor devices are being connected to perform various basic tasks. Different sensors are used in the smart irrigation system which is based on smart controllers and detectors for vigilance of water line, watering efficacy and environment. Automatic leaf disease detection is also a very important concept for monitoring the growth of food crops and other plant leaves with medicinal value. This system also detects the symptoms of the disease in the plant leaves automatically. In the proposed decision-making system the authors have collected a set of betel leaf images and have utilized image content characterization and Support Vector Machine (SVM) classifier. Different stages like initialization, bifurcation, eradication of characteristics and orderly arrangement have been involved in image processing mechanisms for analyzing decisions. At the time of processing, an input image is being rescaled as per requirement. Authors of this research paper have eliminated color and surface characteristics from input image sets for classification and training. The proposed system will be able to analyze the test images automatically for making decisions about the leaf whether it is abnormal or good.

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Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

Cited by 57 publications

References 80 publications

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

The Impact of Tax Reduction and Fee Reduction Based on Big Data Algorithm on the High-Quality Development of the Real Economy under the Action of Coupling Effect or Substitution Effect

An Overview of Supervised Machine Learning Methods and Data Analysis for COVID-19 Detection

Agronomy with IoT Devices: The Smart Solution for Detection of Diseases of Betel Leaves

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