Work

Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model

Year: 2025

Type: article

Abstract: Accurate classification of fetal ultrasound images is critical for early diagnosis, yet remains challenging due to limited labeled data and high inter-class variability. This study presents a robust deep learning framework that combines a MobileNet backbone with multi-head self-attention and LSTM layers to enhance feature learning and temporal context. To address data scarcity and imbalance, unsupervised clustering was employed using Principal Component Analysis (PCA) for dimensionality reduction and K-means (k=4) for pseudo-label generation. These pseudo-labeled clusters were then balanced using oversampling techniques. The proposed model was trained using transfer learning on the augmented dataset and achieved a test accuracy of approximately 98 % with a macro-F1 score of 0.98, indicating highly reliable classification performance.•Employed PCA (100 components) and K-means (k=4) for effective pseudo-labeling and class balancing.•Designed a hybrid deep learning architecture using MobileNet, multi-head attention, and LSTM.•Achieved ∼98 % test accuracy and 0.98 macro-F1 score, demonstrating strong model generalization.

Source: MethodsX

DOI: 10.1016/j.mex.2025.103563

Authors: Aniket K. Shahade, Priyanka V. Deshmukh, Pritam H. Gohatre, Kanchan S. Tidke, Rohan Ingle

Institutions: Symbiosis International University, Visvesvaraya National Institute of Technology

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,260