Abstract
Cancer is a disease caused by an abnormal growth of cells. It is a group of 100 different diseases. This is a menacing disease which can severely affect the quality of life of someone affected by it. It can also take a toll on the emotional well-being of the patient along with physical repercussions. The cells which form malignant tumors can occur in any part of the body but the brain is an area where the chance of survival is minimal if not treated accurately in time. Radiologists and Oncologists make use of MRI scans which provide images of the brain. These images can have different appearances depending on the setting of pulse sequences of the MRI such as T1-W, T2-W, MPR, DWI, and FLAIR. This chapter focuses on the segregation of tumor region based on sequences such as T1-weighted and T2-weighted images of the brain. The segmentation is performed by making use of a fusion of BAT and Interval Type 2 Fuzzy C-Means Clustering (IT2FCM) algorithms which is aimed at simplifying the task of a radiologist. This novel approach can detect tumors of different types, different shapes, at different locations and also the segregation of tissue formations that are present in the brain.
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Kotwal, A., Bharti, R., Pandya, M., Jhaveri, H., Mangrulkar, R. (2021). Application of BAT Algorithm for Detecting Malignant Brain Tumors. In: Dey, N., Rajinikanth, V. (eds) Applications of Bat Algorithm and its Variants. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-5097-3_7
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