Imaging for medical applications is the next challenging task for health care technology. A smart image capturing device with image filtering sensors can sense, supervise and detect diseases. In this work, the focus is on ailment-detecting devices that integrate sensors with accelerated computing capability by offering a practical, precise and low-cost solution for medical diagnosis. An image acquired with a cytoscope contains artifacts that can lead to misdiagnosis of the actual ailment. A Cytoscope equipped with sensors collects data from ultrasound images, filters out unwanted noise and thereby extracts ROIs from medical images using the CANR algorithm. This helps the patients avoid unnecessary complications from diagnosis and saves time in visiting multiple diagnostic centers. An efficient algorithm has been discussed, which involves an array of sensors that extracts the information from ultrasound medical images. In this study, the experimental work was carried out using ultrasound images of abdomen to detect gall stones. The accuracy and success of CANR was methodically inspected and the correctness of the framework was demonstrated. It is able to achieve an efficiency of 91.80% to clear the undesirable noise from an ultrasound image without disturbing the characteristics of an image in healthcare.