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Cancer Diagnosis Enhancement Through Deep Learning in Medical Image-Based Bioinformatics Techniques

Sam M. K.Lima HongouSofia Arkhstan
Volume 4

Abstract

The integration of deep learning into medical image-based bioinformatics has significantly advanced cancer diagnosis by enabling precise, automated analysis of complex imaging data. Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated high accuracy in detecting and classifying various cancer types from imaging modalities such as MRI, CT scans, and histopathological images. These models can identify subtle patterns and anomalies that may be overlooked by human observers, facilitating early detection and improving diagnostic outcomes. Furthermore, the application of deep learning enhances the efficiency of diagnostic workflows, reduces the burden on healthcare professionals, and supports personalized treatment planning by providing detailed insights into tumor characteristics. As research progresses, the continued refinement of deep learning algorithms and their integration with bioinformatics tools hold promise for further improving the accuracy and reliability of cancer diagnostics.

Keywords

Deep Learning, Cancer Diagnosis, Medical Imaging, Bioinformatics, Convolutional Neural Networks, Diagnostic Accuracy

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