In this paper, two Medical image Classification can play an important role in diagnostic and teaching purposes in medicine. For these purposes different imaging modalities are used. There are many classifications created for medical images using both grey-scale and color medical images. One way is to find the texture of the images and have the analysis. Texture classification is an image processing technique by which different regions of an image are identified based on texture properties[4]. Second way is by using neural network classification techniques and the final one is by using the data mining classification schemes. Neural networks play a vital role in classification, with the help of, supervised and unsupervised techniques. The word data mining refers to, extracting the knowledge from large amounts of data. It is one of the area, which uses statistical, machine learning, visualization and other data manipulation with knowledge extraction techniques[10]. This finds an insight into the relationship between the data and patterns hidden in the data. Using the digital data within the pictures actual communication systems creates a possibility for research enhancements. Medical images form a vital component of a patient’s health record and are associated with manipulation, processing and handling of data by computers. This makes the basis for the computer-assisted radiology development. Further developments are associated with the use of decision support systems which helps to decide, the relevant knowledge for diagnosis.high performance full adder circuits are proposed. We simulated these two full adder circuits using Cadence VIRTUOSO environment in 0.18 μm UMC CMOS technology and compared the Power dissipation, time delay, and power delay product (PDP) of the proposed circuits with other 10 transistor full adders. Simulation results show that for the supply voltage of 1.8V, these circuits are suitable for arithmetic circuits and other VLSI applications with very low power consumption and very high speed performance.

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Image classification Texture classification Data mining Association rule mining