Medical Anomaly Detection Using Generative Adversarial Network With Self Attention Mechanism

Fuad, Fahim Abrar; Kaisar, Saqif; Ananta, Tanvir Hassan

Medical Anomaly Detection Using Generative Adversarial Network With Self Attention Mechanism

dc.contributor.author	Fuad, Fahim Abrar
dc.contributor.author	Kaisar, Saqif
dc.contributor.author	Ananta, Tanvir Hassan
dc.date.accessioned	2026-06-24T09:55:25Z
dc.date.issued	2025-10-25
dc.description	Supervised by Dr. Md. HasanulKabir, Professor, Mr. Md. Bakhtiar Hasan, Assistant Professor, Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Board Bazar, Gazipur, Bangladesh This thesis is submitted in partial fulfillment of the requirement for the degree of Bachelor of Science in Computer Science and Engineering, 2025
dc.description.abstract	Anomaly detection in medical imaging plays a vital role in assisting early diagnosis and treatment. Traditional supervised methods rely heavily on annotated abnormal samples, which are often scarce and diverse, making them impractical for real-world deployment. This work explores unsupervised anomaly detection using a range of GAN-based frameworks, where models learn to capture the distribution of normal data and identify deviations without the need for labeled outliers. Various architec tures, including reconstruction-based and feature-matching approaches, are evalu ated and extended with enhancements such as self-attention mechanisms and posi tional encodings to improve spatial feature learning. Extensive experiments on medi cal imaging datasets demonstrate that the proposed techniques significantly improve detection performance, highlighting the effectiveness of adversarial learning for un supervised medical anomaly detection.
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dc.identifier.uri	https://repository.iutoic-dhaka.edu/handle/123456789/2638
dc.language.iso	en
dc.publisher	Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
dc.title	Medical Anomaly Detection Using Generative Adversarial Network With Self Attention Mechanism
dc.type	Thesis

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