Detection of Abnormalities in Brain using Machine Learning in Medical Image Analysis

Sivasangari, A.; SIVAKUMAR, .; Helen, Suji; Deepa, S.; Vignesh,; Suja,

doi:10.1109/icscds53736.2022.9761029

Cited by 2 publications

(2 citation statements)

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“…Yet, these techniques may greatly benefit from advancement. To find brain tumors, Sivasangari et al 16 employed ML and image classifiers. Using magnetic resonance imaging, it was possible to distinguish between diseased and healthy cells.…”

Section: Foundation Workmentioning

confidence: 99%

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Employing a hybrid model based on texture-biased convolutional neural networks and edge-biased vision transformers for anomaly detection of signal bonds

2023

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The railway system of Japan plays a vital role in the national transportation network.A key issue in public transport safety is anomaly detection in railways. Lately, developing robust algorithms and methods for anomaly detection has become the premier task in this field. Recently introduced approaches based on convolutional neural networks, generative adversarial networks, and vision transformers (ViTs) have remarkably improved the research in anomaly detection. Our work proposes a high-performance module for the anomaly detection of signal bonds. First, we present an overview of the proposed module; then, the object detection model and the proposed hybrid classification model based on texture-biased convolutional neural networks and edge-biased ViTs are introduced. Finally, the proposed anomaly detection module is evaluated for accuracy using the dataset from the East Japan Railway Company and the receiver operating characteristic curve. The results show that our proposed module achieves a better performance on real-life data than the two methods that were combined to generate it, raising hope toward possible usage in other areas as well.

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Section: Foundation Workmentioning

confidence: 99%

“…Yet, these techniques may greatly benefit from advancement. To find brain tumors, Sivasangari et al 16 . employed ML and image classifiers.…”

Section: Previous Workmentioning

confidence: 99%