Technology Acceptance Model for Breast Cancer Examination Assistant Using Computer Vision and Speech Recognition
Authors: Robert Kerwin C. Billones, Melvin K. Cabatuan, Laurence Gan Lim, Edwin Sybingco, Jose Santos Carandang VI, Eric Camilo Punzalan, Dennis Erasga, Michael Ples, Romeo Teruel, Balintawak Sison-Gareza, Ma. Ellenita De Castro, Julien Carandang, and Elmer P. Dadios
Abstract
This paper presents the development of a computer-assisted breast cancer examination using computer vision and speech recognition, with focus on user acceptance for improved technology penetration. The study includes the development of algorithms for breast area detection and delineation and hand tracking during palpation. It uses speech recognition and audio feedback for better human–computer interaction during breast self-examination (BSE) performance. The technology acceptance model (TAM) is used in the design concept and implementation of the breast examination assistant to rate its perceived usefulness (PU), perceived ease of use (PEOU), attitude (ATT), and behavioral intention to use (BI). Performance rating is based on Cronbach’s. Computer vision algorithm for BSE, speech recognition, and synthesis results in previous studies were highlighted to associate in TAM considerations. TAM results showed PU is 0.8887 (good), PEOU is 0.7817 (good), ATT is 0.7758 (good), and BI is 0.9378 (excellent).