Deep Learning in Healthcare: Focusing on Interpretability and Data Quality Challenges for Enhanced Disease Detection

This research paper reviewed the applications of deep learning algorithms in the healthcare domain, highlighting their transformative impact on disease diagnosis and treatment. It provided an in-depth analysis of various deep learning architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), and demonstrated their effectiveness in diagnosing diseases such as breast cancer, lung cancer, and Parkinson's disease. Furthermore, the paper discussed the challenges and opportunities associated with the implementation of deep learning in healthcare, focusing on key issues such as data quality, computational requirements, and model interpretability. The study concluded by emphasizing the significant potential of deep learning to enhance diagnostic accuracy and improve treatment efficiency, paving the way for more advanced personalized healthcare solutions.

Keywords:

healthcare , deep learning , machine learning algorithms , supervised learning