Automated Medical Diagnosis with Deep Learning

Abstract

Medical diagnosis is of utmost importance in healthcare, ensuring prompt and accurate disease identification for effective patient care. However, conventional diagnostic approaches relying on manual examination are time-consuming and susceptible to human errors. In recent times, the integration of deep learning has revolutionized medical diagnosis by capitalizing on its capacity to comprehend intricate patterns from complex medical data. This research introduces an automated medical diagnosis system, employing advanced deep learning techniques. The system incorporates Convolutional Neural Networks (CNNs) for medical image analysis and Recurrent Neural Networks (RNNs) for processing patient data. Specifically, the CNN model scrutinizes medical images like X-rays and MRIs, proficiently identifying anomalies indicative of diverse conditions such as lung diseases, bone fractures, or tumors. Meanwhile, the RNN model delves into patient records and medical histories, offering valuable insights into disease progression and personalized treatment strategies. Key Words: Deep Learning, Artificial Intelligence ,Machine Learning, Medical Imaging, Radiology