Faculty Publications
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Item Inverse Estimation of Breast Tumor Size and Location with Numerical Thermal Images of Breast Model Using Machine Learning Models(Taylor and Francis Ltd., 2023) Venkatapathy, G.; Mittal, A.; Gnanasekaran, N.; Desai, V.H.Early screening of cancer plays a vital role in successful treatment. Normally, the temperature of breast surface changes due to the size and location of the underlying tumor. Temperature patterns can be used to estimate the size and location of tumor by an inverse approach using machine learning algorithms. The present study aims to provide an efficient machine learning model that can be relied on to predict the size and location of the tumor using numerical thermal images. There is no availability of actual thermal images labeled with the size and location of the tumor. Consequently, successive numerical simulations are used to develop the numerical thermal image dataset with a three-dimensional breast model solved with COMSOL Multiphysics. The obtained numerical thermal images are uniquely labeled with the corresponding size and location of the tumor and trained using various machine learning regression models such as linear, support vector, K-nearest neighbor, and decision tree regression models. The results are analyzed using parity plots and mean absolute error. The present study found that the decision tree regression model outperformed the other machine learning models and improved the estimation accuracy by rejecting the numerical thermal images which are having slight variation in surface temperature. © 2022 Taylor & Francis Group, LLC.Item AAPFC-BUSnet: Hierarchical encoder–decoder based CNN with attention aggregation pyramid feature clustering for breast ultrasound image lesion segmentation(Elsevier Ltd, 2024) Sushma, B.; Pulikala, A.Breast cancer causes a serious menace to women's health and lives, underscoring the urgency of accurate tumor detection. Detecting both cancerous and non-cancerous breast tumors has become increasingly crucial, with ultrasound imaging emerging as a widely adopted modality for this purpose. However, identifying breast lesions in ultrasound images is a challenging task due to various tumor morphologies, geometry, similar color intensity distributions, and fuzzy boundaries, particularly irregularly shaped malignant tumors. This work proposes an encoder–decoder based U-shaped convolutional neural network (CNN) variant with an attention aggregation-based pyramid feature clustering module (AAPFC) to detect breast lesion regions. The network consists of the U-Net variant as a base network and AAPFC to fuse features extracted at the various levels of the base U-Net using a suitable feature fusion technique. Furthermore, the deformable convolution with adaptive self-attention mechanism is introduced to decode the pyramid features parallel to capture the various geometric features at multi-stages. Two public breast lesion ultrasound datasets consisting 263 malignant, 547 benign and 133 normal images are considered to evaluate the performance of the proposed model and state-of-the-art deep CNN-based segmentation models. The proposed model provides 96% accuracy, 68% Mean-IoU, 97% specificity, 82% sensitivity and 0.747 kappa score respectively. The conducted qualitative and quantitative performance analysis experiments show that the proposed model performs better in breast lesion segmentation on ultrasound images. © 2024 Elsevier Ltd