A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation

Jiang, Huiyan; Diao, Zhaoshuo; Shi, Tianyu; Zhou, Yang; Wang, Feiyu; Hu, Wenrui; Zhu, Xiaolin; Luo, Shijie; Tong, Guoyu; Yao, Yu-Dong

doi:10.1016/j.compbiomed.2023.106726

Cited by 96 publications

(30 citation statements)

References 105 publications

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“…Fitness tracker data are being widely applied to develop the healthcare sector. The IoB can help physicians to offer better healthcare solutions to patients by using information from wearables [63]. It can also assist in understanding people's physiology and evaluating health situations.…”

Section: Iob Utilization For Healthcare Practicesmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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With the swift pace of the development of artificial intelligence (AI) in diverse spheres, the medical and healthcare fields are utilizing machine learning (ML) methodologies in numerous inventive ways. ML techniques have outstripped formerly state-of-the-art techniques in medical and healthcare practices, yielding faster and more precise outcomes. Healthcare practitioners are increasingly drawn to this technology in their initiatives relating to the Internet of Behavior (IoB). This area of research scrutinizes the rationales, approaches, and timing of human technology adoption, encompassing the domains of the Internet of Things (IoT), behavioral science, and edge analytics. The significance of ML in medical and healthcare applications based on the IoB stems from its ability to analyze and interpret copious amounts of complex data instantly, providing innovative perspectives that can enhance healthcare outcomes and boost the efficiency of IoB-based medical and healthcare procedures and thus aid in diagnoses, treatment protocols, and clinical decision making. As a result of the inadequacy of thorough inquiry into the employment of ML-based approaches in the context of using IoB for healthcare applications, we conducted a study on this subject matter, introducing a novel taxonomy that underscores the need to employ each ML method distinctively. With this objective in mind, we have classified the cutting-edge ML solutions for IoB-based healthcare challenges into five categories, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs), deep neural networks (DNNs), multilayer perceptions (MLPs), and hybrid methods. In order to delve deeper, we conducted a systematic literature review (SLR) that examined critical factors, such as the primary concept, benefits, drawbacks, simulation environment, and datasets. Subsequently, we highlighted pioneering studies on ML methodologies for IoB-based medical issues. Moreover, several challenges related to the implementation of ML in healthcare and medicine have been tackled, thereby gradually fostering further research endeavors that can enhance IoB-based health and medical studies. Our findings indicated that Tensorflow was the most commonly utilized simulation setting, accounting for 24% of the proposed methodologies by researchers. Additionally, accuracy was deemed to be the most crucial parameter in the majority of the examined papers.

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Section: Iob Utilization For Healthcare Practicesmentioning

confidence: 99%

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Amiri,

Heidari,

Darbandi

et al. 2023

Sustainability

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“…In recent years, the technology of machine learning has made significant strides in processing images, including recognition, segmentation, and classification 62 . These technologies have been widely used and made significant contributions to the auxiliary diagnosis of organ lesions, especially in the lung, 63 breast, 64 thyroid, 65 and other organs 62 .…”

Section: Analysis Limitations Of Existing Hb Recognition Algorithmsmentioning

confidence: 99%

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Zheng,

Sha,

Deng

et al. 2023

Liver International

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Metabolic dysfunction‐associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time‐consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

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“…Currently, artificial intelligence methods have achieved very bright results in the processing of various types of medical images [17][18][19][20]. The proposed neural network makes feature extraction more intelligent, and it has unparalleled advantages in abstract feature extraction, which makes it highly accurate and robust in image recognition [21,22].…”

Section: Introductionmentioning

confidence: 99%

Combination of fluorescence sensor and artificial intelligence—a new method of quantitative ketamine detection

Zeng,

Fu,

Guo

et al. 2023

Meas. Sci. Technol.

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Drug abuse is currently a growing concern, with the abuse of narcotic drugs, represented by ketamine (KET), being particularly serious. Chronic use and overdose of such drugs can lead to hallucinations and serious health risks. Therefore, it is essential to establish a fast and high-precision detection method for strict control of drug abuse. In this study, we propose a fast detection process for small instruments, and the use of data augmentation and transfer learning techniques makes it possible to build a high-precision detection model based on small data sets. We also propose an EfficientNet-based KET quantitative detection network. We maintain its lightweight architecture while introducing large kernel convolution to obtain a stronger feature representation without increasing the number of parameters. Our proposed detection process is time-saving and effective, reducing the time required to acquire large data sets, and our proposed model predicts all test data perfectly, solving the problem of bias of other networks in predicting low concentration samples, and breaking the limit of optical biosensor detection accuracy from 1ng/ml to 0.1ng/ml.

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A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation

Cited by 96 publications

References 105 publications

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

The Personal Health Applications of Machine Learning Techniques in the Internet of Behaviors

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Combination of fluorescence sensor and artificial intelligence—a new method of quantitative ketamine detection

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