MEDICAL IMAGE ANOMALY DETECTION USING DEEP LEARNING: A HYBRID CNN-VAE APPROACH
Priya Balasubramanian · INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY · 2024
The rapid evolution of deep learning has significantly enhanced medical image analysis, enabling more accurate detection of anomalies such as tumors, lesions, and rare pathologies.This research proposes a hybrid architecture combining Convolutional Neural Networks (CNNs) and Variational Autoencoders (VAEs) to detect anomalies in medical images.By leveraging the spatial feature extraction capabilities of CNNs and the generative modeling power of VAEs, the model excels in unsupervised anomaly detection tasks.Evaluated on datasets such as Brain MRI and Chest X-ray, our hybrid model demonstrates superior accuracy and robustness compared to baseline models.This work contributes an interpretable, scalable solution to support radiologists in early disease diagnosis.