Professors Behnam Kiani Kalejahi and Sajid Khan from the Engineering School of Central Asian University have published their research in the international peer-reviewed journal Journal of Imaging (MDPI), which focuses on imaging technologies, computer vision, and artificial intelligence.
The study presents an artificial intelligence-based framework for medical image analysis aimed at improving the accuracy, efficiency, and reliability of automated disease detection using radiological images. The proposed approach incorporates advanced deep learning techniques to extract relevant image features while preserving clinically significant anatomical information.
A central contribution of the research is the development of a multi-stage learning strategy designed to improve model generalization across different imaging conditions and datasets. By combining advanced feature representation methods with optimized training techniques, the proposed framework enhances diagnostic performance while maintaining computational efficiency.
The researchers evaluated the proposed method using publicly available medical imaging datasets. The experimental results demonstrated improvements in accuracy, robustness, and overall reliability compared with several existing approaches, highlighting the potential of the framework for supporting computer-aided diagnosis and clinical decision-making.
This publication reflects the ongoing research activities of the Engineering School at Central Asian University in the fields of artificial intelligence, deep learning, and medical imaging, contributing to the advancement of digital healthcare technologies and international scientific collaboration.
