Vlsi implementation of optimized neural network controller for cancer detection

Abstract

Recent developments in the medical image analysis have helped the healthcare experts in diagnosing the state and stage of a disease. However, the mortality rate of cancer victims has increased drastically. Therefore, a reliable diagnostic system is of prime importance in the early diagnosis of cancer which eventually reduces the death rate due to cancer. In the present work, various neural network based algorithms have been implemented to accurately detect the presence and also the categorization of breast cancer and skin cancer. A modified K-means was developed to analyze large datasets. This method proved to be a better algorithm for the existing K-means in terms of computation time, which is lesser and accuracy, which is higher. However, the results were not upto the expected level. The novel C-Mantec algorithm was used for the prediction of breast cancer. Support Local Binary Pattern (SLBP) was used to extract the texture features from the mammograms and then the extracted features were used to train the Neural Network. The trained Neural network was able to classify at better accuracy. However, categorization was not processed. An improved C-Mantec algorithm was proposed using a control system based on PID (Proportional Integral Derivative). The combination of the training algorithm with the PID control helped in setting up an intelligent PID control. The analysis exhibited the enhancement of network s generalization capability and improvement in its speed, which in turn strengthened the PID s control newline