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@article{196861,
        author = {Kanish Shandilya and Komal Kashyap and Devansh Dhingra and Pooja Singh},
        title = {Automated Medical Image Analysis for Disease Diagnosis Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {5350-5353},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196861},
        abstract = {This paper presents an automated medical image analysis system for disease diagnosis using deep learning techniques. The proposed system consists of three independent modules designed to perform pneumonia detection, cardiac disease classification, and atrium segmentation from medical images. Convolutional Neural Networks (CNNs) are employed for classification tasks, while a U-Net architecture is utilized for segmentation.
The pneumonia detection model is trained on chest X-ray images and achieves an accuracy of approximately 95–97%. Similarly, the cardiac classification model, trained on processed heart image data, achieves an accuracy in the range of 93–96%. For atrium segmentation, the U-Net model is trained on medical imaging data in NIfTI format, and the performance is evaluated qualitatively through visual comparison of predicted and ground truth masks.
The system is implemented using Python and PyTorch, incorporating standard preprocessing, training, and evaluation techniques. The results demonstrate that deep learning models can effectively assist in medical image analysis tasks by providing reliable and consistent outputs across different tasks.},
        keywords = {Deep Learning, Medical Image Analysis, Convolutional Neural Network, U-Net, Disease Diagnosis, Image Segmentation},
        month = {April},
        }