• M.SUBHASHINI

in Image processing to detect leukemia cells. Method: Here we discussed nonlinear median filter designed using CA by employing quantum dots for removal of impulse noises in image and the denoised image is processed for tamura texture extraction using Fuzzy-c-mean segmentation algorithm. The extracted tamura feature fed as input to back propagation neural network classifier tool for image classification. Findings: The classified results used to detect leukaemia cells. QCA based designs reduces number of logic gates and total area consumption compared to conventional CMOS based designs. All the QCA circuits are implemented in QCA Designer software. Thus, artificial neural network classifies carcinoma and non-carcinoma cells used in breast cancer diagnosis. Classification is performed using Tamura Feature. The features are coarseness, contrast, directionality, line likeness, regularity, roughness. The output is observed as Region of Convergence (True Positive Rate vs False Positive Rate is obtained. It is implemented using MATLAB software. Novelty: It is used in medical diagnosis such as detecting leukaemia cells.

Quantum Dot Cellular Automato, Image Processing, Nano electronics.

"QUANTUM DOT CELLULAR AUTOMATO PROCESS IN IMAGE CLASSIFICATION TO DETECT LEUKAEMIA CELLS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.1259-1272, December 2018, Available :http://www.jetir.org/papers/JETIREB06154.pdf

"QUANTUM DOT CELLULAR AUTOMATO PROCESS IN IMAGE CLASSIFICATION TO DETECT LEUKAEMIA CELLS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp1259-1272, December 2018, Available at : http://www.jetir.org/papers/JETIREB06154.pdf