Optimized Gabor Feature Extraction for Mass Classification Using Cuckoo Search for Big Data E-Healthcare

Abstract

Widespread use of electronic health records is a major cause of a massive dataset that ultimately results in Big Data. Computer-aided systems for healthcare can be an effective tool to automatically process such big data. Breast cancer is one of the major causes of high mortality rate among women in the world since it is difficult to detect due to lack of early symptoms. There is a number of techniques and advanced technologies available to detect breast tumors nowadays. One of the common approaches for breast tumour detection is mammography. The similarity between the normal (unaffected) tissues and the masses (affected) tissues is often very high that leads to false positives (FP). In the field of medicine, the sensitivity to false positives is very high because it results in false diagnosis and can lead to serious consequences. Therefore, it is a challenge for the researchers to correctly distinguish between the normal and affected tissues to increase the detection accuracy. Radiologists use Gabor filter bank for feature extraction and apply it to the entire input image that yields poor results. The proposed system optimizes the Gabor filter bank to select most appropriate Gabor filter using a metaheuristic algorithm known as “Cuckoo Search”. The proposed algorithm is run over sub-images in order to extract more descriptive features. Moreover, feature subset selection is used to reduce feature size because feature extracted from the segmented region of interest will be high dimensional and cannot be handled easily. This algorithm is more efficient, fast, and less complex and spawns improved results. The proposed method is tested on 2000 mammograms taken from DDSM database and outperforms some of the best techniques used for mammogram classification based on Sensitivity, Specificity, Accuracy, and Area under the curve (ROC).