Paper Title
Multi-Disease Diagnostic System Using AI-ML

Abstract
This research introduces an advanced Multi-Disease Diagnostic System driven by Artificial Intelligence and Machine Learning (AI-ML), employing Convolutional Neural Networks (CNN), Convolutional Recurrent Neural Networks (CRNN), and the Jordan-Elman model architectures. The system targets the automated identification of critical conditions including Pneumonia, Tuberculosis(TB),COVID-19,BreastCancer,andPolycystic Ovary Syndrome (PCOS), using diverse medical imaging modalities like chest X-rays, mammograms, and ultrasound scans. By training on curated datasets, the models achieve high diagnostic precision, with performance assessed using accuracy, precision, recall, and F1 score. This AI-powered framework is designed to enhance early detection, alleviate clinical workload, and optimize diagnostic work flows demonstrating the trans formative potential of intelligent systems in modern healthcare. Keywords - Machine Learning, Convolutional Recurrent Neural Networks (CRNN), Healthcare Automation