Abstract
Bone cancer (BC) affects the majority of the elderly in today’s world. It directly affects the neurotransmitters and leads to dementia. MRI images can spot bone irregularities related to mild cognitive damage. It can be useful for predicting bone cancer, though it is a big challenge. In this research, a novel technique is proposed to detect bone cancer using AdaBoost classifier with a hybrid ant colony optimization (ACO) algorithm. Initially, MRI image features are extracted, and the best features are identified by the AdaBoost curvelet transform classifier. The proposed methods yield better classification accuracy of 97% on analyzing MRI images and detecting the bone tumor in it. Three metrics, namely accuracy, specificity, and sensitivity, are used to evaluate the proposed method. Based on the results, the proposed methods yield greater accuracy than the existing systems.
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