Adaptive FLAME based segmentation and classification for bone cancer detection

Abstract

Bone cancer, also known as bone sarcoma, is a rare cancer that grows abnormal tissue in bones. This malignancy is highly likely to metastasize. Because of this, early classification and detection of bone cancer are now the most essential variables in predicting a patient's cure. An adaptive fuzzy clustering by local approximation of mEmbership (AFLAME) was developed as a method for investigating a potential strategy for identifying bone cancer in this body of work. For a wide variety of applications, accurate classification and segmentation of bone tumors are absolutely necessary steps. However, getting there has been tough because many methods, like medical imaging techniques, don't have enough non-homogeneous and contrast intensity to accomplish the goal. This makes progress toward the objective more challenging. Support vector machine (SVM) classifiers are used to complete the classification process. In this study, we provide a new method for segmenting bone cancer, opening up new avenues of inquiry into this important topic.