## 1 Introduction
The LIPTEW can provide forced oscillation on the sample surface, and the behavior reveals surface mechanical properties, as is achieved by palpation. Photoacoustic imaging (PAI), which is based on the same LIPTEW principle as L-RFA, uses the penetration of laser radiation into the body to achieve a higher spatial resolution of imaging using ultrasound [36; 37]. This imaging is achieved by transmitting a nanosecond laser pulse into the blood vessels and measuring the propagation of ultrasound waves induced by the absorption of laser pulses by hemoglobin in the blood. Therefore, PAI is based on a detection technique, and is a different scheme from the elastic modulus measurement categorized as elastography. Similarly, the acoustic tweezers method [38; 39; 40; 41] uses ultrasound waves to measure hardness, but as with optical and magnetic tweezers, its single-cell approach makes it impossible to measure while maintaining organ geometry. The L-RFAs conducted in previous studies were realized using high-power pulsed lasers. A sweep pulse excitation method was demonstrated, in which the repetition frequency of the laser pulse was used to realize a low average power evaluation [42; 43]. In this new frequency-sweep pulse excitation method, L-RFA evaluation was demonstrated with a metal disk and a low laser power of several milliwatts [43]. Therefore, this L-RFA technique is applicable to the stiffness diagnoses of soft tissue tumors by palpation using LIPTEW. Using a laser as the excitation source, excitation energy can be transmitted into a single microscopic cell in the order of optical wavelengths in a non-contact, quantitative manner while maintaining the macroscopic structure of the organ shape. Combined with optical fiber transmission technology, the system can be mounted on an endoscope, and is therefore a less invasive in vivo measurement method. In this study, we evaluated the characteristics of LIPTEW on agarose gels with different Young's moduli to simulate soft tissues toward single-cell hardness measurements while maintaining organ geometry. The results obtained from this study suggest that the machine learning-based analysis method for the prediction of the Young's modulus using L-RFA data can contribute to computer-aided detection (CADe). The proposed evaluation method using L-RFA demonstrates significant potential as a critical measurement technique required in the process of physical oncology for the evaluation of cell stiffness while preserving the shape of the organ. This method allows for the realization of elastic modulus imaging techniques by performing two-dimensional (2D) scanning. When cancer diagnosis based on combined multiple data is a critical data point, tumor cell stiffness presented by L-RFA diagnostic techniques based on big data analysis is essential for CADe. The dependence of laser-induced elastic waves on the stiffness, spatial resolution in measurements, and data analysis performance based on machine learning, as proposed in this study, represents a performance milestone for future soft-tissue tumor stiffness diagnostic and stiffness imaging technologies. The rest of the paper is arranged as follows. Section 2 describes the measurement and analysis method using L-RFA. Section 3 presents the results. Section 4 discusses the advantages of this method, and Section 5 presents the conclusions of our study.