Deep-learning Diffuse Optical Tomography (DL-DOT) is a non-invasive diagnostic method that uses near-infrared radiation and deep-learning algorithms to image soft tissues in the body, such as the breast. However, DL-DOT studies have limitations, such as using only homogeneous or semihomogeneous datasets for the forward problem, which can lead to predictions not being accurate when used on experimental measurements. Another limitation regarding DL-DOT is the severe overfitting of the prediction model observed when DL methods are employed for DOT image reconstruction. To overcome this challenge, a regularized nested UNet++ deep-learning algorithm is employed. The proposed method effectively solves the DOT inverse problem in inhomogeneous breasts by applying a regularization technique. This technique reduces overfitting and simplifies the prediction model. Results show that when the regularized neural network is used to detect tumors, a minimal mean square error (MSE) loss of 5.16 × 10−3 is achieved compared to a non-regularized MSE loss of 4.18 × 10−2. The enhancement of close to one order of magnitude shown by the proposed method demonstrates the significance of regularization neural networks in breast tumor detection and improving the accuracy of DOT image reconstruction.
Infant head injuries and damage can be caused by various factors such as tumors or physical trauma. The treatment for a head injury will depend on the severity of the damage. Nevertheless, the infant’s head should be imaged before any treatment. High-density diffuse optical tomography (HD-DOT) is a non-invasive imaging technology that can be employed for subsurface imaging of the infant brain. However, there are problems with HD-DOT, such as low resolution, ill-posedness of the inverse problem, and high computational costs. In this study, to improve subsurface imaging of the infant head, an extreme gradient boosting (XGBoost) algorithm is combined with HD-DOT. The proposed method is then used to detect subsurface anomalies in the infant head. The proposed method achieves a similarity index greater than 0.97 in terms of cosine similarity and less than 0.12 in terms of the root mean square error, demonstrating its effectiveness. Moreover, the proposed method requires a minimal dataset compared to conventional deep learning methods and consumes significantly less time to train. The results of this study suggest that the proposed method can provide a promising alternative for subsurface imaging of the infant head, which could significantly impact the medical imaging field in the future.
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