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Accurate diagnosis of cancer plays an important role in order to save human life. The results of the diagnosis indicate by the medical experts are mostly differentiated based on the experience of different medical experts. This problem could risk the life of the cancer patients. A fast and effective method to detect the lung nodules and separate the cancer images from other lung diseases like tuberculosis is becoming increasingly needed due to the fact that the incidence of lung cancer has risen dramatically in recent years and an early detection can save thousands of lives each year. The focus of this paper is to compare the performance of the ANN and SVM classifiers on acquired online cancer datasets. The performance of both classifiers is evaluated using different measuring parameters namely; accuracy, sensitivity, specificity, true positive, true negative, false positive and false negative.

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Modified Fuzzy C-Means (MFCM) algorithm, which is a combination of fuzzy c means and k means, for medical image segmentation, suffers from high computational time and noise which lead to high memory consumption and low accuracy. Therefore, this paper presents an Enhanced MFCM algorithm (ACOMFCM), which has better accuracy and low memory consumption for medical image segmentation. Thirty medical images used in this work were pre-processed using Gaussian filtering method. ACOMFCM algorithm was developed using Ant Colony Optimization techniques to minimise the Euclidean distance between the data point and centre coordinate in the K-means algorithm characterizing the existing MFCM. Segmentation of the thirty images were carried out using the MFCM and ACOMFCM algorithms in Matrix Laboratory 7.1 (R0011a) environment. Performance of the MFCM and the ACOMFCM was evaluated using segmentation accuracy, segmentation time and memory consumption. The average result of MFCM algorithm for thirty images yielded segmentation accuracy, segmentation time and memory consumption of 7117992.29, 22.484s and 494161920bit, respectively while average result of the ACOMFCM algorithm for thirty images used yielded segmentation accuracy, segmentation time and memory consumption of 10590135.79, 6.649s and 502960128 bit, respectively.