Optical Deformation of Biological Cells using Dual-Beam Laser Tweezer.

Optical tweezer is a non-contact tool to trap and manipulate microparticles such as biological cells using coherent light beams. In this study, we utilized a dual-beam optical tweezer, created using two counterpropagating and slightly divergent laser beams to trap and deform biological cells. Human embryonic kidney 293 (HEK-293) and breast cancer (SKBR3) cells were used to characterize their membrane elasticity by optically stretching in the dual-beam optical tweezer. It was observed that the extent of deformation in both cell types increases with increasing optical trapping power. The SKBR3 cells exhibited greater percentage deformation than that of HEK-293 cells for a given trapping power. Our results demonstrate that the dual-beam optical tweezer provides measures of cell elasticity that can distinguish between various cell types. The non-contact optical cell stretching can be effectively utilized in disease diagnosis such as cancer based on the cell elasticity measures.

Convolutional Neural Network Based Breast Cancer Histopathology Image Classification.

Breast cancer is a global health concern, with approximately 30 million new cases projected to be reported by 2030. While efforts are being channeled into curative measures, preventive and diagnostic measures also need to be improved to curb the situation. Convolutional Neural Networks (CNNs) are a class of deep learning algorithms that have been widely adopted for the computerized classification of breast cancer histopathology images. In this work, we propose a set of training techniques to improve the performance of CNN-based classifiers for breast cancer identification. We combined transfer learning techniques with data augmentation and whole image training to improve the performance of the CNN classifier. Instead of conventional image patch extraction for training and testing, we employed a high-resolution whole-image training and testing on a modified network that was pre-trained on the Imagenet dataset. Despite the computational complexity, our proposed classifier achieved significant improvement over the previously reported studies on the open-source BreakHis dataset, with an average image level accuracy of about 91% and patient scores as high as 95%.Clinical Relevance- this work improves on the performance of CNN for breast cancer histopathology image classification. An improved Breast cancer image classification can be used for the preliminary examination of tissue slides in breast cancer diagnosis.