Enhancing Disease Diagnosis Through Deep Learning in Medical Imaging

DOI

10.2991/978-94-6463-738-0_13How to use a DOI?

Keywords

Diagnosis; Medical Imaging; ADABOOST Ensemble Learning; Deep Learning Models; CNNs, or convolutional neural networks

Abstract

In contemporary healthcare, when medical imaging is essential to early detection and action, an accurate and fast disease diagnosis is crucial. DL models include convolutional neural networks (CNNs), have shown impressive performance in automating image processing tasks such as anomaly detection and disease classification. However, their performance can be hindered by challenges such as imbalanced datasets, noise in medical images, and feature redundancy, leading to suboptimal diagnostic accuracy and generalization issues. To address these limitations, this study introduces a novel diagnostic framework that integrates the ADABOOST ensemble learning technique with DL models. ADABOOST works by combining multiple weak classifiers into a strong predictive model, iteratively improving its accuracy by assigning greater weights to misclassified samples. This approach mitigates overfitting, enhances bias correction, and ensures robust performance across diverse datasets. Key metrics including accuracy, sensitivity, and specificity are significantly improved by the suggested approach when tested on benchmark medical imaging datasets. The results underscore the potential of leveraging ADABOOST to complement DL models, addressing critical challenges in medical image classification. This hybrid framework offers a reliable and efficient solution for improving diagnostic reliability in clinical settings, setting the stage for further advancements in automated healthcare diagnostics.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. Mahendran
AU  - P. Vandana
AU  - R. Priyangi
PY  - 2025
DA  - 2025/06/22
TI  - Enhancing Disease Diagnosis Through Deep Learning in Medical Imaging
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 150
EP  - 159
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_13
DO  - 10.2991/978-94-6463-738-0_13
ID  - Mahendran2025
ER  -