Faculty Publications
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Item Estimation of Breast Tumor Parameters by Random Forest Method with the Help of Temperature Data on the Surface of the Numerical Breast Model(Springer Science and Business Media Deutschland GmbH, 2023) Venkatapathy, G.; Rahul, V.M.; Gnanasekaran, N.The second most frequent reason for cancer-related fatalities in women is breast cancer. When a condition is identified early, better treatment choices are available. Different temperature patterns are seen on the breast surface due to the tumors, which change blood perfusion rate and metabolic heat production. Thermography is an infrared imaging technology for breast cancer screening that records temperature variations. The temperature dataset on the surface of the breast that corresponded to the tumor’s diameter and the location was needed for the current study, but such actual data are not accessible. Thus, the modeling and development of a dataset constitute the initial component of the current study. The bio-heat transport equation is solved using COMSOL multiphysics software, and the model consists of a spherical tumor inside of a hemispherical breast model. By changing the sizes and positions of the tumor inside the breast during simulations, a reliable dataset is created. The training and testing of the dataset produced from the simulations using the random forest method make up the second portion of the current study. Breast skin temperature is used as an input in a random forest machine learning algorithm in the current work to determine the diameter and location of the tumor inside the breast. The diameter and area of the tumor location are estimated by a trained random forest algorithm. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Estimation of tumor parameters using neural networks for inverse bioheat problem(Elsevier Ireland Ltd, 2021) Majdoubi, J.; Iyer, A.S.; Ashique, A.M.; Arumuga Perumal, D.A.; Mahrous, Y.M.; Rahimi-Gorji, M.; Issakhov, A.Background and objective: Some types of cancer cause rapid cell growth, while others cause cells to grow and divide at a slower rate. Certain forms of cancer result in visible growths called tumors. This work proposes an inverse estimation of the size and location of the tumor using a feedforward Neural Network (FFNN) model. Methods: The forward model is a 3D model of the breast induced with a tumor of various sizes at different locations within the breast, and it is solved using the Pennes equation. The data obtained from the simulation of the bioheat transfer is used for training the neural network. In order to optimize the neural network architecture, the work proposes varying the number of neurons in the hidden layer and thus finding the best fit to create a relationship between the temperature profile and tumor parameters which can be used to estimate the tumor parameters given the temperature profile. Results: These simulations resulted in a temperature distribution profile that could thus be used to locate and determine the parameters of the cancerous tumor within the breast. The prediction accuracy showed the capacity of the trained Feed Forward Neural Network to estimate the unknown parameters within an acceptable range of error. The model validations use the Root Mean Square Error method to quantify and minimize the prediction error. Conclusions: In this work, a non-intrusive method for the diagnosis of breast cancer was modelled, which yields conclusive results for the estimation of the tumor parameters. © 2021