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@article{174454,
        author = {Khushbu Nirmalkar and Dr. Sunil B. Mane},
        title = {Medical Anomaly Detection for Lungs Images},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {4551-4558},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=174454},
        abstract = {It becomes more interesting through with respect to images & multimedia processing in computer vision and medical image analysis where we use Deep Anomaly Detectors (based on AI) to capture anomalies times more accurately. The quick spread of digital content and the visual data complication make it a must to have strong anomaly detection methods in place to affirm data integrity and security. However, classical methods have not achieved parity with this more sophisticated appreciation for the subtleties in visual data. In this work, we fill in this gap by introducing a new method based on an ensemble of deep learning and anomaly detection, employing well-known neural networks architectures pretrained on various datasets. This strategy applied to medical imaging, especially for chest X- rays, can make the highest efficiency (diagnostic performance) in model prediction, achieving overfitting and model is easy to transfer relatively poor, through hierarchically stacked CNN structures. The experimental results show that both of our proposed methods outperform many existing methods for lung tumor and diseases detection, which means our methods are very effective for lung abnormality detection.},
        keywords = {CNN, RNN, Image Processing},
        month = {March},
        }