Hybrid Image Processing Methods for Medical Image Examination

 Diseases occurring externally in the human body are quite easy to detect and treat when compared with the diseases found in internal organs TPE gloves. A pre-screening procedure is recommended as essential to detect the disease in its premature phase. If the disease and its severity are identified in its premature phase, a treatment procedure could be recommended and implemented to control and cure the disease. This could be could imply less effort compared to a disease diagnosed in its delayed phase Disposable Aprons. 

Diseases found in external body organs, such as the eye and skin, could be examined with a personal checkup by an experienced doctor along with an image supported detection system for further examination. Meanwhile, diseases of internal organs such as the brain, lungs, heart, breast, the digestive system, and blood are normally diagnosed using a chosen imaging method associated with a prescribed imaging modality. The imaging procedure followed for the internal organ needs complete monitoring and should be examined in a controlled environment with a prescribed clinical protocol. 

 


After registering the image of the organ YICHANG Gloves, the disease can be diagnosed using a computerized disease examination procedure or a personal check by a clinical expert. In most cases, semi-automated/automated disease detection procedures are implemented to speed up the diagnostic process. The report prepared with these techniques are used as supporting evidence regarding the patient, which will help the doctor during decision making and treatment planning process. 

Further, the availability of the computing facility helps to develop a large number of computeraided detection procedures, which considerably reduce the burden on doctors during conventional disease detection and also during mass screening processes. This book aims to discuss the details of the Artificial-Intelligence (AI) based disease detection procedures mainly developed by Machine-Learning (ML) and Deep-Learning (DL) techniques. 

Further, this book also presents the details of Hybrid Image Processing (HIP) methods implemented to enhance the detection accuracy for a class of clinical images.

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