Advancing Medical Diagnostics with Deep Learning: A Novel Approach to Disease Detection and Prediction
DOI:
https://doi.org/10.63876/ijtm.v2i2.109Keywords:
Deep learning, medical diagnostics, disease prediction, convolutional neural networks, healthcare AIAbstract
Deep learning has revolutionized various fields, including medical diagnostics, by enabling more accurate and efficient disease detection and prediction. This paper explores the latest advancements in deep learning applications for medical diagnostics, emphasizing how convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer models enhance diagnostic accuracy. The study discusses the integration of deep learning with medical imaging, electronic health records (EHRs), and genomic data to improve early disease detection and personalized treatment strategies. Additionally, ethical considerations, challenges, and future directions in deep learning-based diagnostics are analyzed. The findings highlight the potential of deep learning to transform healthcare by reducing diagnostic errors, optimizing treatment plans, and improving patient outcomes.
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