The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.
IJACSA research on disease prediction and diagnosis using machine learning and AI — covering heart, liver, kidney, respiratory, skin, and infectious disease prediction models.
Published in International Journal of Advanced Computer Science and Applications (IJACSA) · list last refreshed August 2026
Lung cancer is among the deadliest cancers worldwide, largely because it is usually caught too late. Building AI tools for earlier, stage-aware diagnosis is hard in practice: patient scans are scattered across hospitals…
This work presents a Model-Augmented Masked Autoencoder (MAE) framework, augmented with domain-adaptive denoising and multi-level feature fusion, for self-supervised representation learning in MRI-based brain tumor analy…
Lung diseases are detected effectively by Chest CT Medical im-ages. Chest CT images are 3D, and they are used for detecting abnormalities in the lungs like pneumonia, tuberculosis, tumors, etc. Chest CT images suffer fro…
Automated epileptic seizure detection from electroencephalogram (EEG) recordings remains a challenging biomedical signal analysis task because of the nonlinear, non-stationary, and high-dimensional characteristics of neu…
Heart disease remains one of the leading causes of mortality worldwide, motivating the development of accurate and scalable screening-support tools. This study presents an integrated framework for heart-disease status cl…
This research examines the challenges of AI adoption in Saudi Arabia healthcare sector in terms of the framework defined by the Vision 2030 healthcare transformation agenda. We utilized the Analytic Hierarchy Process (AH…
Pneumoconiosis remains a major occupational lung disease among workers exposed to silica, coal, and other in-organic dusts. Although chest X-ray screening is widely used in clinical practice, diagnostic performance is of…
With the widespread adoption of the Internet of Medical Things (IoMT), hospitals have become prime targets for cyberattacks. To overcome the limitations of traditional defenses and computationally heavy deep learning mod…
Foliar pathogens destroy roughly a third to two-fifths of harvestable crops each year, yet the deep convolutional classifiers that have proven most effective at automated leaf-image diagnosis carry parameter counts in th…
Subtyping inflammatory bowel disease (IBD) from gut microbiome sequencing data is a clinically demanding problem. Cross-cohort variability is large, and models trained on one dataset often fail on another. In this work,…
