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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: In modern medical diagnostics, Deep Learning models are commonly used for illness diagnosis, especially over X-ray chest images. Deep Learning approaches provide unmatched promise for early identification, prognosis, and treatment evaluation across a range of illnesses, by combining sophisticated algorithms with large datasets. It is crucial to research these models to lead to improved ones to progress toward disease identification's precision, effectiveness, and scalability. This paper presents the study of a CNN+VGG19 Deep Learning architecture (subsets of machine learning), both before and after its modification. The same dataset is used over the existing and modified models to compare metrics under the same conditions. They are compared using metrics like loss, accuracy, precision, sensitivity, and AUC. These metrics display lower values in the updated model than in the original one. The numbers demonstrate the occurrence of the overfitting phenomenon, which is most likely the result of the model's increased complexity for a small dataset. The noise in the images included in the dataset may also be the cause. As a result, it can be stated that regularization techniques should be applied; otherwise, layers of extraction and classification should not be added to the model to prevent overfitting.
Elma Zanaj, Lorena Balliu, Gledis Basha, Elunada Gjata and Elinda Kajo Meçe, “Studying the Behavior of a Modified Deep Learning Model for Disease Detection Through X-ray Chest Images” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150585
@article{Zanaj2024,
title = {Studying the Behavior of a Modified Deep Learning Model for Disease Detection Through X-ray Chest Images},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150585},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150585},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Elma Zanaj and Lorena Balliu and Gledis Basha and Elunada Gjata and Elinda Kajo Meçe}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.