Anomaly Detection in Healthcare : Brain Tumor

Anomaly detection plays a critical role in healthcare by enabling the early identification and diagnosis of medical conditions, such as brain tumors, which significantly impact patient outcomes. This study focuses on developing and evaluating advanced techniques for detecting brain tumor anomalies using medical imaging modalities like MRI and CT scans. Leveraging deep learning, machine learning, and statistical methods, the proposed approach seeks to enhance sensitivity and specificity in tumor detection. We explore various algorithms, including con- volutional neural networks (CNNs) and auto encoders, to identify subtle anomalies and differentiate between malignant and benign tumors. Our results demonstrate improved diagnostic accuracy, reduced false positives, and greater robustness in diverse clinical scenarios. This research highlights the transformative potential of anomaly detection systems in healthcare, offering a pathway toward more precise, timely, and cost-effective brain tumor diagnostics.