Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

Abstract

Radiological imaging plays a pivotal role in disease diagnosis, but the manual interpretation is prone to errors and time-consuming. This research explores the utilization of deep learning algorithms for real-time disease detection and classification in radiological images, aiming to enhance diagnostic accuracy and efficiency in healthcare settings. Deep learning algorithms offer a promising solution by automating the detection and classification process, potentially reducing diagnosis time and improving patient outcomes. However, deploying deep learning algorithms for real-time disease detection in radiological images presents several challenges. These challenges also consist of the need for diverse and large datasets for model training, addressing class imbalance and data variability, ensuring robustness to noise and artifacts, and interpreting model decisions for clinical validation. To overcome these challenges, this research proposes several methods, including data augmentation techniques for increasing dataset diversity, transfer learning from pre-trained models for leveraging existing knowledge, ensemble learning for combining multiple models for improved performance, and attention mechanisms to focus on relevant image regions. Additionally, techniques for uncertainty estimation and model interpretability are explored to enhance trust and acceptance of automated diagnosis systems in clinical practice. By addressing these challenges and implementing appropriate methods, deep learning algorithms show promise for real-time disease detection and classification in radiological images, offering a transformative approach to medical imaging analysis. Results show an accuracy of 97%, sensitivity and specificity at 97% and 95%, and an F1 score of 96%.