Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model
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 la...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | MethodsX |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125004078 |
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| author | Aniket K. Shahade Priyanka V. Deshmukh Pritam H. Gohatre Kanchan S. Tidke Rohan Ingle |
| author_facet | Aniket K. Shahade Priyanka V. Deshmukh Pritam H. Gohatre Kanchan S. Tidke Rohan Ingle |
| author_sort | Aniket K. Shahade |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c8f8294da73c49c2ba7c4b8f591ba294 |
| institution | Kabale University |
| issn | 2215-0161 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj-art-c8f8294da73c49c2ba7c4b8f591ba2942025-08-20T04:03:25ZengElsevierMethodsX2215-01612025-12-011510356310.1016/j.mex.2025.103563Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM modelAniket K. Shahade0Priyanka V. Deshmukh1Pritam H. Gohatre2Kanchan S. Tidke3Rohan Ingle4Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India; Corresponding author.Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, IndiaVisvesvaraya National Institute of Technology, Nagpur, IndiaDr. Rajendra Gode Institute of Technology & Research, Amravati, IndiaSymbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, IndiaAccurate 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.http://www.sciencedirect.com/science/article/pii/S2215016125004078Fetal ultrasoundImage classificationPseudo-labelingPCAK-means clusteringMobileNet |
| spellingShingle | Aniket K. Shahade Priyanka V. Deshmukh Pritam H. Gohatre Kanchan S. Tidke Rohan Ingle Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model MethodsX Fetal ultrasound Image classification Pseudo-labeling PCA K-means clustering MobileNet |
| title | Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model |
| title_full | Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model |
| title_fullStr | Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model |
| title_full_unstemmed | Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model |
| title_short | Method for fetal ultrasound image classification using pseudo-labelling with PCA-KMeans and an attention-augmented MobileNet-LSTM model |
| title_sort | method for fetal ultrasound image classification using pseudo labelling with pca kmeans and an attention augmented mobilenet lstm model |
| topic | Fetal ultrasound Image classification Pseudo-labeling PCA K-means clustering MobileNet |
| url | http://www.sciencedirect.com/science/article/pii/S2215016125004078 |
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