Computational intelligence on image classification methods for microscopic image data

摘要

Technology play a substantial function in exemplifying technical potentials that are unconsidered in the past to include intelligent behaviour. Computers in machine learning involve the modelling of intelligent behaviour with the least human mediation. Computational intelligence signifies a set of vital information processing methods for information managing and decision accomplishing. Major critical trials in technical exploration is the supervision of data obtained from various resources. Presently, Scanning Electron Microscopy (SEM) images are categorized as per user's measures, which greatly restrict misuse and reliability, owing for the need in definite classification algorithm. The foremost objective is to evaluate the exactness with each algorithm in terms of performance measures such as accuracy, precision, sensitivity, F-measure, kappa, computational time and confusion matrix. In this work, a recital assessment among three classification algorithms on SEM image dataset was conducted. Experimental results revealed that the Random forest algorithm gives the highest accuracy comparing to other two approach. Hence, the researchers would be able to perform automatic SEM image classification evading the necessity to categorize image and for providing a searchable database to identify a definite group of images.