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International Journal Of Electrical, Electronics And Computers(IJEEC)

Anomaly Detection in Medical Images Using SMOTE Algorithm: A Comprehensive Approach

Rajesh Rajaan , Loveleen Kumar , Kailash Soni , Mani Butwall


International Journal of Electrical, Electronics and Computers (IJECC), Vol-9,Issue-5, September - October 2024, Pages 15-19, 10.22161/eec.95.2

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Article Info: Received: 12 Sep 2024; Accepted: 09 Oct 2024; Date of Publication: 15 Oct 2024

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Anomaly detection in medical imaging is pivotal for early diagnosis and treatment planning. However, the inherent class imbalance in medical datasets poses significant challenges, often leading to biased models that underperform on minority classes. This study investigates the integration of the Synthetic Minority Over-sampling Technique (SMOTE) with various machine learning and deep learning models to enhance anomaly detection in medical images. By applying SMOTE to balance datasets and evaluating its impact across multiple models, we demonstrate improved detection accuracy, sensitivity, and specificity. The findings underscore the efficacy of SMOTE in addressing class imbalance, thereby enhancing the reliability of anomaly detection systems in medical imaging.

Anomaly Detection, Medical Imaging, SMOTE, Class Imbalance, Machine Learning, Deep Learning

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