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@article{203584,
        author = {Sudiksha Asati and Dr.Ekta  Soni and Dr.Sakshi Kathuria and Dr.Riya Sapra and Dr.Sarita Gulia},
        title = {A Deep Learning-Based Framework for Automated Disease Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11341-11348},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203584},
        abstract = {Artificial Intelligence (AI) has become an influential technology in the medical field for enhancing disease identification and forecasting. This research paper presents a multiple disease identification system that uses AI and Machine Learning (ML) and Deep Learning (DL) techniques to accurately and efficiently identify various diseases. The proposed machine learning system uses patient symptoms, lab test results, and medical imagery data, such as X-rays and MRI scans, to diagnose different diseases, including diabetes, heart disease, pneumonia, kidney disease and liver disease, simultaneously. Random Forest and Support Vector Machine (SVM) are used for symptom-based prediction while Convolutional Neural Networks (CNNs) are employed for medical image analysis. The system further includes cloud computing and Internet of Things (IoT) innovations for instant healthcare monitoring and remote access. The results indicate that the performance of the proposed AI model is superior to traditional disease detection methods in terms of prediction accuracy and diagnostic efficiency. This technique reduces the amount of manual work, helps identify the disease at an early stage, and enhances decision-making in health care systems. In this study, the application of artificial intelligence for creating smart diagnostic tools in health care systems has been emphasized.},
        keywords = {Artificial Intelligence, Machine Learning, Deep Learning, Multiple Disease Detection, Healthcare System, Convolutional Neural Network, Medical Image Analysis, Disease Prediction, IoT, Cloud Computing},
        month = {May},
        }