Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{181000,
        author = {Mohammed Furqan Ur Rehman and Dr. Mohammed Arshad and Omer Ahmed Quadri and Mirza Shareef Baig},
        title = {Machine Learning-Based Dual Diagnostic System for Medical Imaging and Symptom-Based Disease Prediction},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {3785-3789},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181000},
        abstract = {Diagnostic accessibility and accuracy have greatly increased as a result of the application of machine learning (ML) and artificial intelligence (AI) in healthcare. A dual-input diagnostic system that combines symptom-based disease prediction and medical image analysis is presented in this project. While machine learning classifiers analyse patient-reported symptoms to suggest possible diseases, convolutional neural networks (CNNs) are used to classify medical images like brain scans and chest X-rays. For increased prediction accuracy, the symptom analysis pipeline is improved by Natural Language Processing (NLP) techniques. The system is trained on publicly accessible benchmark datasets to guarantee scalability, dependability, and efficiency. It is implemented in Python using frameworks such as TensorFlow, Keras, OpenCV, and Scikit-learn. The goal of this integrated solution is to assist medical professionals with rapid, initial evaluations, particularly in settings with limited resources.},
        keywords = {Convolutional Neural Networks, Artificial Intelligence, Machine Learning, Medical Image Analysis, Natural Language Processing, Symptom-Based Diagnosis},
        month = {June},
        }