Researches Advanced in Medical Detection based on Deep Learning

ABSTRACT

Medical image analysis based on computer vision technology has always been a research hotspot in the community, which aims to assist doctors in diagnosis by accurately analyzing pathological images to divide the patient's condition and the patient's lesions. Thanks to the rapid development of deep learning, the application of computer image recognition technology in medicine is becoming more and more widespread, while still facing a series of challenges such as low data set data, insufficient performance of algorithms and fine delineation of lesions. In order to solve these problems, based on extensive literature research, this paper first compares the algorithms in the application for Corona Virus Disease 2019, skin cancer and liver cancer. Then we introduce the improvement of these algorithms by expanding the number of data sets, optimizing the algorithms, and fitting the neural networks and models, whcih can improve the accuracy of image recognition technology to assist doctors in identifying lesions in clinical practice. The algorithms are further compared quantitatively on the basis of the training set in clinical diseases, and the difficulties to be overcome in image recognition and the future development trend are explained and predicted from the analysis of the comparison. Many new algorithms and excellent models are being gradually improved with the development of the times, and image recognition technology will also develop towards more research fields in the future. © 2023 SPIE.