Deep learning techniques for medical image segmentation & classification
Keywords:
deep learning, convolutional neural networks, medical images, segmentation, classification, detectionAbstract
Imaging in medicine plays a significant part in a broad number of clinical applications, including those that are utilised for early detection, monitoring, diagnosis, and assessment of therapy for a wide variety of medical diseases. Deep learning and artificial neural networks are two concepts that you need to have a firm grasp on if you want to become an expert in medical image analysis using computer vision. Rapid progress is being made in the field of research known as deep learning approach (DLA), which focuses on medical image processing. DLA has had widespread use in the field of medical imaging as a diagnostic tool for determining the presence or absence of disease. Along with the construction of artificial neural networks and a comprehensive investigation of DLA, some of the potential applications for medical imaging are covered in this article. Digital pictures from X-rays, CT scans, mammograms, and histology are the primary focus of the majority of DLA applications. This article offers an in-depth analysis of the research that has been done on DLA for the classification, detection, and segmentation of medical images.
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