ADVANCEMENTS IN DISEASE DETECTION THROUGH NEURAL NETWORKS IN MEDICAL IMAGE ANALYSIS

Abstract

The use of neural networks, specifically convolutional neural networks (CNNs), in medical image processing has resulted in substantial breakthroughs in illness identification. This study digs into the use of neural networks to analyze medical images and identify disorders, emphasizing the transformative influence these technologies have had on medical diagnostics. By leveraging deep learning architectures such as ResNet, Inception, and DenseNet, researchers have achieved substantial improvements in the accuracy and efficiency of disease identification across various imaging modalities, including MRI, CT, X-ray, and ultrasound. In-depth analysis of neural networks' function in vital tasks such organ segmentation, tumor detection, and pathology categorization is provided by this study. It is clear from a thorough examination of these applications that deep learning models can perform better than conventional image analysis methods, providing increased accuracy and quicker processing times. This study highlights the critical contributions that neural networks have made to the area by demonstrating their capacity to process medical images with intricate patterns and minute variations that are frequently difficult for traditional techniques to handle. Additionally, this study discusses the advantages and disadvantages of applying deep learning to medical picture processing. Important topics like data scarcity, model generalization, and interpretability are covered in detail. Interpretability is still a major challenge since neural networks' "black box" nature can make it difficult for physicians to completely trust and utilize these technologies because it obscures the decision-making process. The study highlights ongoing efforts to enhance the transparency and explainability of neural networks, aiming to build more robust and interpretable models. Model generalization is yet another important topic this study examines. For a neural network to be clinically useful, it must function effectively on a variety of imaging devices and patient demographics. This paper examines many approaches to enhance generalization, such as utilizing extensive and varied datasets and sophisticated training methods. One major obstacle is the lack of data, especially when it comes to rare disorders. The study addresses methods to lessen this problem, including transfer learning, data augmentation, and the creation of synthetic data using strategies like generative adversarial networks (GANs). This survey offers a comprehensive overview of the quickly developing subject of neural network applications in medical imaging by incorporating important findings from reviews and prominent papers. It highlights how deep learning has the potential to revolutionize the healthcare industry and shows how better patient outcomes can result from more advanced diagnostic capabilities. The study demonstrates not only the present successes but also the potential for neural networks to transform disease diagnosis in the future. In the end, this study adds to our knowledge of how neural networks are changing the way that diseases are identified. It makes a strong argument for the application of deep learning technologies in clinical settings and provides information on potential future developments and advancements that could improve medical diagnostics even further. Through the continued development and refinement of neural network models, the potential to achieve more accurate, efficient, and accessible healthcare becomes increasingly attainable, heralding a new era in medical image analysis and disease detection.