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AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework

  • S.I.: Intelligent Systems in Biomedical and Healthcare Informatics
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Abstract

Adaptive self-learning is a promising technique in medical image analysis that enables deep learning models to adapt to changes in image distribution over time. As medical image data can vary due to factors like imaging equipment and patient demographics, adaptive self-learning becomes valuable for maintaining the accuracy and robustness of deep learning frameworks. Initially trained on a large dataset, the framework can adapt to new modalities using transfer learning, adaptive learning, and incremental learning, incorporating both manual and auto (CNN-based) features. Adaptive self-learning offers various benefits, including improved model accuracy and efficiency, reducing the need for manual retraining. However, challenges such as the risk of overfitting, acquiring relevant manual features, and careful monitoring need to be addressed. Combining manual features and pretrained CNN models can enhance performance in medical image analysis tasks such as tumor classification, lesion detection, and cancer segmentation. Manual features capture specific image characteristics, while pretrained CNN models automatically learn abstract features from extensive datasets. Combining these approaches provides additional information that neither approach alone can capture. In this study, we present a unique framework for adaptive self-learning in medical image analysis and classification. The proposed framework analyzes image modality, applies preprocessing techniques, and acquires both manual and CNN-based pertinent features. However, careful tuning and experimentation are essential to determine the optimal combination of manual features with the appropriate CNN model architecture. The recommended adaptive self-learning framework achieves a high averaged F1-score \(97.35\pm 0.39\) and precision of \(97.19 \pm 0.51\), as well as its fantabulous abstraction and accuracy of \(97.81 \pm 0.35\), implying that it might be used to build a pathologist’s aid tool.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors extend their appreciation to the Deputyship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work through the project no. (IFKSUOR3–596–3).

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Authors and Affiliations

  1. Department of Computer Science, Faculty of Information Technology and Computer Science, University of Central Punjab, Lahore, Punjab, 54000, Pakistan

    Saeed Iqbal

  2. Faculty of Arts, Society and Professional Studies, Newman University, Birmingham, UK

    Adnan N. Qureshi

  3. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh, 11543, Saudi Arabia

    Khursheed Aurangzeb, Musaed Alhussein & Syed Irtaza Haider

  4. Laboratoire Biomécanique et Bioingénierie UMR 7338, Center de Recherches de Royallieu, Université de Technologie de Compiègne, Rue du Dr Schweitzer, 60200, Compiègne, France

    Imad Rida

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  1. Saeed Iqbal
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Iqbal, S., Qureshi, A.N., Aurangzeb, K. et al. AMIAC: adaptive medical image analyzes and classification, a robust self-learning framework. Neural Comput & Applic (2023). https://doi.org/10.1007/s00521-023-09209-1

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