Abstract: The rapid development of medical practices and imaging technology tools creates substantial growth in the amount of medical image data each year in our present era. This research aims to develop a hybrid approach that integrates Machine Learning (ML) and Deep Learning (DL) techniques to enhance the accuracy and reliability of medical image classification for diagnostic purposes. Medical imaging data complexity and growing volume serve as the research motivation, which leads to an investigation of standalone ML or DL limitations and their combination into a single framework. The medical image processing starts with normalization, then noise reduction, and continues to grayscale conversion before performing histogram equalization. This research uses VGG16 and ResNet50 alongside MobileNet and InceptionV3 for feature extraction, then applies ten different ML algorithms, including SVM and MLP, and Ran-dom Forest, for classification. Five public medical image datasets from Kaggle are used: COVID-19 chest X-rays, melanoma skin lesions, pneumonia chest X-rays, acute stroke facial images, and various eye diseases. Hybrid models display superior performance compared to stand-alone ML or DL models based on accuracy, precision, recall, and F1-score evaluation measures. Multiple datasets demonstrate that the MobileNet+MLP combination de-livers the most accurate results, which demonstrates its reliable and efficient performance. The developed AI diagnostic tool presents a scalable system alongside accuracy and interpretability to enhance clinical decision outcomes.

Raed Alazaidah, Moath Alomari, Hamza Mashagba, Musab Iqtait, Azlan B. Abd Aziz, Hayel Khafajeh, Omar Khair Alla Alidmat, Ghassan Samara, Haneen Alzoubi and Samir Salem Al-Bawri. “Medical Diagnosis Using Hybrid of Machine Learning and Deep Learning Techniques”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.01612135

@article{Alazaidah2025,
title = {Medical Diagnosis Using Hybrid of Machine Learning and Deep Learning Techniques},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.01612135},
url = {http://dx.doi.org/10.14569/IJACSA.2025.01612135},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Raed Alazaidah and Moath Alomari and Hamza Mashagba and Musab Iqtait and Azlan B. Abd Aziz and Hayel Khafajeh and Omar Khair Alla Alidmat and Ghassan Samara and Haneen Alzoubi and Samir Salem Al-Bawri}
}