Sidds - Scalable Intelligent Disease Diagnostic System

         SIDDS: Scalable Intelligent Disease Diagnostic System

Abstract:

Diagnosing any disease in advance that is at an early stage is beneficial because it allows the doctors to decide which course of action is to be taken for treatment. Contrary to this, if these diagnosing techniques - the forefront of which is medical imaging- are used improperly or in access, then this results in the wastage of precious medical facilities as well as puts the patients’ safety at risk. It is beneficial to everyone, the practitioners as well as the patients. It is profitable, as well as it has become extremely important for timely diagnosis. A histrionic propagation can be seen in the size of diagnostic medical imaging.

The main and commonly used forms of medical imaging include X-Rays, CT scan (Computed Tomography), Magnetic Resonance imaging, commonly known as MRI, and nuclear imaging such PET and SPET. Identifying any biological disorder, is a major problem in the medical domain while it is a grayscale image. This project proposes a technique for detection and colorization which is used for the purpose of detecting various kinds of diseases, disorders or variations from the normal cases.

It is worth mentioning that all these tasks are being completed more or less by using the current technologies. Our efforts however, are directed towards the early detection of diseases and illness, before they can progress to a state noticeable in the medical imaging techniques. A seemingly humble endeavor, this project has the potential for a vast scope, and subsequently can prove to be helpful, if it becomes technically more advance and sophisticated. We will be mainly using the three basic techniques of image processing that is, segmentation, which will facilitate the tumor, cancer or abnormality detection, followed by colorization.

At the completion of the project, we will be able to detect a disease in a body organ, (for example lung cancer or brain tumor), and colorize the diseased area for better and clearer visualization.

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