(untitled) (International Journal of Engineering and Computer Science, 2017)
Artificial neural networks have featured in a wide range of medical fields, often with promising results. This paper reports on a review benefit of artificial neural networks (ANNs) as decision making tools in the field of cancer. Artificial neural network are used to find relationship between input and output or recognized pattern in data. Here we have to recognized cancer cell pattern. This addresses the system which achieves cell characterization for finding percentage of cancer cells in the given image with high accuracy. Harris corner detection Algorithm itself scans the whole image and performs the classification of cancer cell. Artificial neural networks are used to aggregate the analyzed data from these images to produce a diagnosis prediction with high accuracy instantaneously where digital images serve as tool for input data. Hence in the process of surgery these automated systems help the surgeon to identify the infected parts or cells in case of cancerous growth of cells to be removed with high accuracy hence by increasing the probability of survival of a patient. In this proposal one of such an automated system for cancer cell classification which helps as a tool assisting surgeon to differentiate cancerous cells from those normal cells i.e. percentage of carcinoma cells, instantaneously during the surgery. Here the pathological images serve as input data. Finally, algorithm was applied to selected pathological images for classification. This design can be extended to estimate the number of carcinoma cells per unit area.