Detection of hydrocephalus using deep convolutional neural network in medical science

Baloni, Dev; Verma, Shashi Kant

doi:10.1007/s11042-022-11953-w

Cited by 4 publications

(3 citation statements)

References 37 publications

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“…in (1), if S j > θ, then x j = 1; otherwise, x j = 0, where θ is the threshold. The perceptron's output can be "trained" to match the desired output by adjusting the weights on the connections between layers.…”

Section: Perceptronmentioning

confidence: 99%

“…Artificial neural networks (ANN) are the building blocks of AI technologies, which simulate the human brain's analyzing and processing abilities to solve complex problems. The unique characteristics of ANN (such as efficient data handling, low complexity, reduced computation, and storage requirements) have enormous potential for a wide range of disciplines, including medical sciences [1] (especially in the areas of cardiology [2], radiology [3], oncology [4], urology [5]), veterinary [6],…”

Section: Introductionmentioning

confidence: 99%

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Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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Artificial neural networks (ANNs) ability to learn, correct errors, and transform a large amount of raw data into beneficial medical decisions for treatment and care has increased in popularity for enhanced patient safety and quality of care. Therefore, this paper reviews the critical role of ANNs in providing valuable insights for patients’ healthcare decisions and efficient disease diagnosis. We study different types of ANNs in the existing literature that advance ANNs’ adaptation for complex applications. Specifically, we investigate ANNs’ advances for predicting viral, cancer, skin, and COVID-19 diseases. Furthermore, we propose a deep convolutional neural network (CNN) model called ConXNet, based on chest radiography images, to improve the detection accuracy of COVID-19 disease. ConXNet is trained and tested using a chest radiography image dataset obtained from Kaggle, achieving more than 97% accuracy and 98% precision, which is better than other existing state-of-the-art models, such as DeTraC, U-Net, COVID MTNet, and COVID-Net, having 93.1%, 94.10%, 84.76%, and 90% accuracy and 94%, 95%, 85%, and 92% precision, respectively. The results show that the ConXNet model performed significantly well for a relatively large dataset compared with the aforementioned models. Moreover, the ConXNet model reduces the time complexity by using dropout layers and batch normalization techniques. Finally, we highlight future research directions and challenges, such as the complexity of the algorithms, insufficient available data, privacy and security, and integration of biosensing with ANNs. These research directions require considerable attention for improving the scope of ANNs for medical diagnostic and treatment applications.

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Section: Perceptronmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

et al. 2023

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“…Yanfen et al [ 19 ] proposed a vision‐based system to detect various objects and to predict the intention of pedestrians for autonomous driving. Baloni et al [ 23 ] introduced a deep convolutional neural network (DCNN) detection method to identify hydrocephalus, a disease found in the central nervous system and requires an early‐stage treatment. For pharmaceutical products, Kandpal et al [ 24 ] summarized the application of hyper spectroscopy, vibrational spectroscopy (Raman, FTIR, IR), [ 25 ] infrared, [ 26 ] and other spectroscopy techniques.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Visual Detection of Defects from Battery Electrode Manufacturing

Choudhary

Clever

Ludwigs

et al. 2022

Advanced Intelligent Systems

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The increasing global demand for high‐quality and low‐cost battery electrodes poses major challenges for battery cell production. As mechanical defects on the electrode sheets have an impact on the cell performance and their lifetime, inline quality control during electrode production is of high importance. Correlation of detected defects with process parameters provides the basis for optimization of the production process and thus enables long‐term reduction of reject rates, shortening of the production ramp‐up phase, and maximization of equipment availability. To enable automatic detection of visually detectable defects on electrode sheets passing through the process steps at a speed of 9 m s−1, a You‐Only‐Look‐Once architecture (YOLO architecture) for the identification of visual detectable defects on coated electrode sheets is demonstrated within this work. The ability of the quality assurance (QA) system developed herein to detect mechanical defects in real time is validated by an exemplary integration of the architecture into the electrode manufacturing process chain at the Battery Lab Factory Braunschweig.

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Utility of Machine Learning in the Management of Normal Pressure Hydrocephalus: A Systematic Review

et al. 2023

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Detection of hydrocephalus using deep convolutional neural network in medical science

Cited by 4 publications

References 37 publications

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Neural Networks for the Detection of COVID-19 and Other Diseases: Prospects and Challenges

Autonomous Visual Detection of Defects from Battery Electrode Manufacturing

Utility of Machine Learning in the Management of Normal Pressure Hydrocephalus: A Systematic Review

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