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Soft tissue abnormalities are often correlated with a change in the mechanical properties of the soft tissue. New developing non-invasive techniques with the ability of early detection of cancerous tissue with high accuracy is a challenging state of art. In this paper, a new method is proposed to investigate the liver tissue cancers. Hyperelastic behavior of a porcine liver tissue has been extracted from the in vitro stress-strain experimental tests of the tissue. Hyperelastic coefficients have been used as the input of the Abaqus FEM software and the palpation of a physician has been simulated. The soft tissue contains a tumor with specified mechanical and geometrical properties. Artificial tactile sensing capability in tumor detection and localization has been investigated thoroughly. In mass localization we have focused on deeply located tumor which is a challenging area in the medical diagnosis. Moreover, tumor type differentiation which is commonly achieved through pathological investigations is studied by changing the stiffness ratio of the tumor and the tissue. Results show that the new proposed method has a high ability in mass detection, localization and type differentiation.

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Blood pressure monitoring is a vital component of maintaining overall health. High blood pressure values, as a risk factor, can lead to heart attacks, strokes, and heart and kidney failures. Similarly, low blood pressure values can also be dangerous, causing dizziness, weakness, fainting, and impaired oxygen delivery to organs, resulting in brain and heart damage. Consequently, continuous monitoring of blood pressure levels in high-risk individuals is very important. A Holter blood pressure monitoring device is prescribed for many patients due to its ability to provide long-term and valuable blood pressure data. The pursuit of software techniques and the development of cuffless blood pressure measurement devices, while ensuring patient comfort and convenience, are among the significant challenges that researchers are focusing on. In this study, a deep learning framework based on the UNet network is proposed for continuous blood pressure estimation from photoplethysmography signals. The proposed model was evaluated on the UCI database, involving 942 patients under intensive care, and achieved mean absolute errors of 8.88, 4.43, and 3.32, with standard deviations of 11.01, 6.18, and 4.15, respectively, for systolic, diastolic, and mean arterial blood pressure values. According to the international BHS standard, the proposed method meets grade A criteria for diastolic and mean blood pressure estimations and grade C for systolic blood pressure estimation. The results of this study demonstrate that the suggested deep learning framework has the necessary potential for blood pressure estimation from PPG signals in real-world applications.