An improvement for the automatic classification method for ultrasound images used on CNN
Kuldoshbay Avazov, Akmalbek Abdusalomov, Mukhriddin Mukhiddinov, Nodirbek Baratov, Fazliddin Makhmudov, Young Im Cho
Abstract
It is no secret today that quality software has a higher superiority than leading technology solutions in computer vision. Remarkable advancement has been achieved in ultrasound image classification, essentially because of the availability of large-scale annotated datasets and deep convolutional neural networks (CNN). Applying CNN in the sphere of medicine is also becoming an active and attractive research area for researchers. In this paper, we introduce an efficient method for the classification of fetal ultrasound images using CNN. To classify these images, we collected four types of fetal ultrasound images from hospitals and internet sources. We first analyze and evaluate various CNN models such as AlexNet, Inception_v3, and MobileNet_v1 for training and testing. Then, the results of these CNN models are quantitatively compared with the proposed model in accuracy and speed. The results show that the proposed classification method can be recognized faster without compromising performance and adjust the ultrasound image parameters quickly and automatically. The proposed CNN model’s weight size is less than 1[Formula: see text]Mb and can be used on mobile or embedded operating systems. We also developed and tested the application on the Android operating system-based mobile device.