Disease Recognition in Medical Images Using CNN-LSTM-GRU Ensemble, a Hybrid Deep Learning

This study introduces a pioneering web application development framework poised to revolutionize healthcare management, elevate patient outcomes, and optimize disease diagnosis processes. Leveraging cutting-edge deep learning techniques, pretrained models, and a user-friendly interface, this framework equips medical professionals with a potent toolset for precise and efficient decision-making. By fusing deep learning methodologies such as CNN-LSTM and CNN-GRU, the framework adeptly captures both spatial and temporal nuances in sequential medical data. Its primary focus lies on the accurate diagnosis of prevalent diseases, including lung cancer, pneumonia, and tuberculosis. The integration of ensemble techniques enhances the reliability of disease classification, achieving remarkable accuracy, precision, and recall rates across diverse illness categories. Furthermore, the development of an intuitive user interface ensures that healthcare practitioners can harness the power of this technology without the need for extensive technical expertise. This accessibility empowers clinicians to expedite critical decisions with unprecedented efficiency and accuracy, ultimately improving patient care and clinical outcomes. This study serves as a testament to the transformative potential of state-of-the-art technologies within the medical domain. As the application is scalable and adaptable to accommodate growing medical image datasets, it paves the way for the continuous evolution of more effective and efficient disease detection systems. In an era marked by technological advancement, this innovation marks a significant stride towards the future of healthcare, where advanced deep learning solutions play a pivotal role in transforming patient care and diagnosis.