Detection of Diseases Using Machine Learning Image Recognition Technology in Artificial Intelligence

Huang, Jian; Li, Jing; Li, Zheming; Zhu, Zhu; Shen, Chen; Qi, Guoqiang; Yu, Gang

doi:10.1155/2022/5658641

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…This article's innovation is underscored by an extensive exploration of ML and image recognition techniques. The application of machine-learning-based image recognition to identify white blood cells establishes a foundation for leveraging this technology in disease diagnosis [7].…”

Section: Related Workmentioning

confidence: 99%

“…V=b0+b1⋅x1+b2⋅x2+...+bn⋅xn (7) Use the obtained coefficients to predict the dependent variable (y) based on new or unseen values of the independent variables (x1,x2,...,xn). The combined application of Simple Linear Regression and Multi Linear Regression in the prediction process signifies a comprehensive approach to disease classification (Eq.…”

Section: Y=b0+b1⋅xmentioning

confidence: 99%

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HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

Kumar,

Thakur,

Mishra

2024

Preprint

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Detecting heart disease is significant for timely intervention and improved patient outcomes. With the rising global burden of cardiovascular disorders, there is an urgent need for accurate and efficient diagnostic tools. Advanced technologies, such as Machine Learning (ML) and Artificial Intelligence (AI), have shown promise in enhancing early detection and risk assessment. Integrating these innovative approaches into clinical practice can significantly contribute to the early identification of heart disease, facilitating timely interventions and personalized patient care. Addressing this need is vital for reducing the impact of heart disease on public health. This research presents a comprehensive examination of heart disease detection models, encompassing neural networks, backpropagation, random crop techniques, Long Short-Term Memory (LSTM), and regression methods. The investigation included a comparative analysis of various models, including Heart Disease Evaluation (HBE), AdaBoost, Support Vector Machine (SVM), Random Forest, and proposed method (HDRP), with a focus on accuracy, error rates, and precision over 5 and 10 epochs. Notably, the HDRP model emerged as a standout performer, consistently demonstrating superior accuracy and the lowest error values, showcasing its efficacy in heart disease prediction. The discussion highlights the unique features of HDRP, attributed to advanced methodologies or distinctive design elements. The study emphasizes the importance of future research to delve into the specific attributes contributing to HDRP's performance, exploring model architecture, optimization strategies, and scalability across diverse datasets.

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Section: Related Workmentioning

confidence: 99%

Section: Y=b0+b1⋅xmentioning

confidence: 99%

HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

Kumar,

Thakur,

Mishra

2024

Preprint

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“…74,75 At present, machine algorithms can be roughly divided into three categories: supervised learning, unsupervised learning, and reinforcement learning. They play a crucial role in many application scenarios such as image recognition, [76][77][78] natural language processing, [79][80][81] traffic prediction, [82][83][84] medical diagnosis [85][86][87] and so on. In recent years, LIBS combined with machine learning algorithms has become a hot topic.…”

Section: Case Studiesmentioning

confidence: 99%

In-Situ Online Detection Of Atmospheric Volatile Organic Compounds Based On Laser Induced Breakdown Spectroscopy: A Review

Liu

2023

At.Spectrosc.

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This review's main purpose is to provide a succinct overview of recent developments in the field of volatile organic compounds (VOCs) detection based on Laser-induced breakdown spectroscopy (LIBS). VOCs are important air pollutants, which have great harm to the environment and human body. It is of great significance to realize the rapid detection of VOCs in the atmospheric environment. LIBS is a novel atomic emission spectroscopy technology, which can achieve the rapid in-situ detection of substances and shows great potential in the online monitoring of atmospheric VOCs. To illustrate the development and difficulties of LIBS technology in atmospheric VOCs detection, some typical cases for various aspects are listed, including the detection of harmful elements in VOCs, source tracing of VOCs, the identification of isomers, and the detection of VOCs in living environment.

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“…The extensive application of artificial intelligence techniques [ 13 ] in the medical field has increased objectivity in the interpretation of the relationship between tongue features and diseases. Image processing [ 14 ] has been utilized to extract information from tongue images and capture physical features that cannot be detected by human eyes. Deep learning [ 15 ] has been applied to automatically identify and learn tongue features.…”

Section: Introductionmentioning

confidence: 99%

A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

Zhu

Jiu-zhang

et al. 2022

Micromachines

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Gastric cancer has become a global health issue, severely disrupting daily life. Early detection in gastric cancer patients and immediate treatment contribute significantly to the protection of human health. However, routine gastric cancer examinations carry the risk of complications and are time-consuming. We proposed a framework to predict gastric cancer non-invasively and conveniently. A total of 703 tongue images were acquired using a bespoke tongue image capture instrument, then a dataset containing subjects with and without gastric cancer was created. As the images acquired by this instrument contain non-tongue areas, the Deeplabv3+ network was applied for tongue segmentation to reduce the interference in feature extraction. Nine tongue features were extracted, relationships between tongue features and gastric cancer were explored by using statistical methods and deep learning, finally a prediction framework for gastric cancer was designed. The experimental results showed that the proposed framework had a strong detection ability, with an accuracy of 93.6%. The gastric cancer prediction framework created by combining statistical methods and deep learning proposes a scheme for exploring the relationships between gastric cancer and tongue features. This framework contributes to the effective early diagnosis of patients with gastric cancer.

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Detection of Diseases Using Machine Learning Image Recognition Technology in Artificial Intelligence

Cited by 8 publications

References 24 publications

HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

HDRP: Integrated Strategies for Robust Heart Disease Detection using Random Crop LSTM Processing and Regression Models

In-Situ Online Detection Of Atmospheric Volatile Organic Compounds Based On Laser Induced Breakdown Spectroscopy: A Review

A Framework to Predict Gastric Cancer Based on Tongue Features and Deep Learning

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