Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information

Karkanis, S.A.; Magoulas, George D.; Theofanous, N.

doi:10.1080/13645700009169652

Cited by 23 publications

(4 citation statements)

References 29 publications

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“…CADe systems focus on locating lesions or abnormalities in medical images, while CADx systems specialize in characterizing these lesions, such as distinguishing between benign and malignant tumors. Using CAD systems in medical imaging is crucial in improving diagnostic precision and efficiency for healthcare professionals [51,52]. In addition, the CADe system has demonstrated a high detection rate for colonic polyps and early gastric and colonic cancers, bridging the gap between OA J Applied Sci Technol, 2024 experienced and less experienced endoscopists [53][54][55].…”

Section: Diagnosis and Treatment Planningmentioning

confidence: 99%

Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare

2024

OA J Applied Sci Technol

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The twenty-first century has witnessed significant advancements in informatics, reshaping our understanding of data processing and accessibility. Artificial intelligence (AI), encompassing techniques such as machine learning (ML), deep learning (DP), and neural networks (NN), is poised to revolutionize medicine. AI holds the capability of analyzing vast amounts of data, extracting meaningful insights, and making accurate predictions, thereby empowering industries to make informed decisions, drive innovation, and enhance efficiency. The landscape of medical AI has evolved significantly, demonstrating expert-level disease detection from medical images and promising breakthroughs across various industries. AI revolutionizes medical practice by leveraging advanced algorithms and machine learning capabilities to improve diagnostics, treatment planning, and overall patient care. However, the deployment of medical AI systems in regular clinical practice still needs to be tapped, presenting complex ethical, technical, and human-centered challenges that must be addressed for successful implementation. While AI algorithms have shown efficacy in retrospective medical investigations, their translation into practical medical settings has been limited, raising concerns about their usability and interaction with healthcare professionals. Moreover, the representativeness of retrospective datasets in real-world medical practice is subject to filtering and cleaning biases. Integrating AI into clinical medicine holds great promise for transforming healthcare delivery, improving patient care, and revolutionizing aspects such as diagnosis, treatment planning, drug discovery, personalized treatment, and medical imaging. With advanced algorithms and machine learning capabilities, AI and robotics in Healthcare can analyze large volumes of medical data, extract meaningful insights, and provide accurate predictions, empowering healthcare professionals to make informed decisions and optimize resource allocation. The availability of extensive clinical, genomics, and digital imaging data, coupled with investments from healthcare institutions and technology giants, underscores the potential of AI in healthcare. This review article explores AI's powerful potential to revolutionize healthcare delivery across multiple domains, emphasizing the need to overcome challenges and harness its transformative capabilities in clinical practice.

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Section: Diagnosis and Treatment Planningmentioning

confidence: 99%

Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare

2024

OA J Applied Sci Technol

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“…We obtained a sample of one of these imaging data sets. In this dataset textures from normal and abnormal tissue samples were randomly chosen from four frames of the same video sequence without applying any preprocessing to the data [12]. Feature extraction was performed using the method of co-occurrence matrices [9].…”

Section: Colonoscopic Video Sequencingmentioning

confidence: 99%

Exponentially weighted moving average charts for detecting concept drift

Ross¹,

Adams²,

Tasoulis³

et al. 2012

Pattern Recognition Letters

320

194

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Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an Exponentially Weighted Moving Average (EWMA) chart to monitor the misclassification rate of an streaming classifier. Our approach is modular and can hence be run in parallel with any underlying classifier to provide an additional layer of concept drift detection. Moreover our method is computationally efficient with overhead O(1) and works in a fully online manner with no need to store data points in memory. Unlike many existing approaches to concept drift detection, our method allows the rate of false positive detections to be controlled and kept constant over time.

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“…We further investigate the performance of the proposed method on a dataset concerned with the automated detection of tumours in colonoscopic video sequences [52]. The accurate online classification of imaging data can contribute to the early detection of colorectal cancer precursors, and assist in the early diagnosis of colorectal cancer.…”

Section: Automated Tumour Detectionmentioning

confidence: 99%

“…The accurate online classification of imaging data can contribute to the early detection of colorectal cancer precursors, and assist in the early diagnosis of colorectal cancer. In the dataset textures from normal and abnormal tissue samples were randomly chosen from four frames of the same video sequence without applying any preprocessing to the data [52]. Feature extraction was performed using the method of co-occurrence matrices [53].…”

Section: Automated Tumour Detectionmentioning

confidence: 99%

: An adaptive classifier for data streams

Pavlidis

Tasoulis

Adams

et al. 2011

Pattern Recognition

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a b s t r a c tStreaming data introduce challenges mainly due to changing data distributions (population drift). To accommodate population drift we develop a novel linear adaptive online classification method motivated by ideas from adaptive filtering. Our approach allows the impact of past data on parameter estimates to be gradually removed, a process termed forgetting, yielding completely online adaptive algorithms. Extensive experimental results show that this approach adjusts the forgetting mechanism to maintain performance. Moreover, it might be possible to exploit the information in the evolution of the forgetting mechanism to obtain information about the type and speed of the underlying population drift process.

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Image recognition and neuronal networks: Intelligent systems for the improvement of imaging information

Cited by 23 publications

References 29 publications

Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare

Revolutionizing Medical Practice: The Impact of Artificial Intelligence (AI) on Healthcare

Exponentially weighted moving average charts for detecting concept drift

: An adaptive classifier for data streams

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