Detection of multiple tumors through temperature response of a human brain using inverse bioheat transfer based on swarm optimization

Abstract

In this article, an innovative inverse regularized framework has been proposed for a bio-medical problem to detect tumors in the human brain via temperature. Most of the thermal data obtained through infrared thermography is sensitive towards errors, thus a regularized approach is sought from the literature. The best inversion algorithm, WOA, together with a hybrid GWOCS are utilized. Pennes model is used for the formulation of heat transfer within the human brain. Using inverse analysis, the unknown blood perfusion rate ωi is retrieved in the regularized environment. The current research reported that at positions where tumor cells are present, the temperature rises. Moreover, with an increase in time, the temperature of the cancer cells increases, whereas no change in the temperature of tissue without tumor is seen. This observation marks the presence of tumor. Elastic net regularization (λ=0.1, α=10−2) for WOA and (λ=0.9, α=10−4) for GWOCS shows the least relative error. After regularization, the perfusion rate is retrieved. A clear distinction of two tumors from the brain tissue is observed. The obtained perfusion rate is used to reconstruct the temperature profile. An excellent matching of the reconstructed temperature field and the exact field is obtained, even when the forward data contain measurement errors. The obtained set of perfusion rate is appropriate for tumor detection, up to 7% error in the measured data. Thus, the proposed comparative procedure is found suitable for the bio-heat transfer problem.