article · Wasit Journal of Computer and Mathematics Science
Medical image processing using magnetic resonance imaging, computed tomography, ultrasound, and X-ray visualisation plays a vital role in diagnosing pathological conditions. Healthcare institutions increasingly look to artificial intelligence to refine image processing and alleviate workloads for physicians and medical personnel. Deep learning methods offer substantial support across medical imaging tasks, including segmentation, classification, disease diagnosis, image generation, image transformation, and image enhancement. These techniques assist specialists by enabling the early detection of diseases, the analysis of tumour localisation behaviours, and the prediction of malignant conditions. Ultimately, integrating deep learning helps clinicians select appropriate treatment strategies, speeds up the diagnostic process, improves overall diagnostic accuracy, and supports better patient health outcomes through timely and tailored care.
Medical scans like X-rays and magnetic resonance imaging generate vast volumes of complex data. Applying deep learning to analyse these images helps healthcare staff detect diseases and tumours earlier and more accurately. Faster, highly reliable interpretation reduces workload pressures on clinical teams and ensures patients receive tailored, timely treatments that can substantially improve healthcare outcomes.
The review highlights deep learning applications for healthcare providers and diagnostic software developers, targeting tools that automate image classification, segmentation, and enhancement across common clinical modalities. The abstract outlines broad capabilities, including tumour localisation and treatment planning support, rather than evaluating specific proprietary products or implementation stages. Consequently, the work reflects an overview of research concepts rather than a near-market technology ready for immediate clinical deployment.
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In healthcare, medical image processing is considered one of the most significant procedures used in diagnosing pathological conditions. Magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and X-ray visualization have been used. Health institutions are seeking to use artificial intelligence techniques to develop medical image processing and reduce the burden on physicians and healthcare workers. Deep learning has occupied an important place in the healthcare field, supporting specialists in analysing and processing medical images. This article will present a comprehensive survey on the significance of deep learning in the areas of segmentation, classification, disease diagnosis, image generation, image transformation, and image enhancement. This survey seeks to provide an overview of the significance of deep learning in the early detection of diseases, studying tumor localization behaviors, predicting malignant diseases, and determining the suitable treatment for a patient. This article concluded that deep learning is of great significance in improving healthcare, enabling healthcare workers to make diagnoses quickly and more accurately, and improving patient outcomes by providing them with appropriate treatment strategies.
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DOI: 10.31185/wjcms.178
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