Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Fatima Rauf; Muhammad Attique Khan; Ali Kashif Bashir; Kiran Jabeen; Ameer Hamza; Ahmed Ibrahim Alzahrani; Nasser Alalwan; Anum Masood

doi:10.3389/fmed.2023.1330218

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes

Front Med (Lausanne). 2023 Dec 20:10:1330218. doi: 10.3389/fmed.2023.1330218. eCollection 2023.

Authors

Fatima Rauf¹, Muhammad Attique Khan¹, Ali Kashif Bashir², Kiran Jabeen¹, Ameer Hamza¹, Ahmed Ibrahim Alzahrani³, Nasser Alalwan³, Anum Masood^{4

5}

Affiliations

¹ Department of Computer Science, HITEC University, Taxila, Pakistan.
² Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, United Kingdom.
³ Computer Science Department, Community College, King Saud University, Riyadh, Saudi Arabia.
⁴ Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
⁵ Institute of Neurosciences and Medicine (INM), Forschungszentrum Jülich, Jülich, Germany.

Abstract

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

Keywords: biomedical imaging; bottleneck layers; deep learning; maternal fetal; optimization; residual architecture.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work is funded by the Researchers Supporting Project number (RSP2023R157), King Saud University, Riyadh, Saudi Arabia.