The Power and Precision of Journalistic Translation
Assistant Professor Dr.Lway Faisal is a researcher from the Computer Science Department at Cihan University Sulaimaniya who has contributed in a high-impact study in Scientific Reports (impact factor 4.3, Q1 Scopus-indexed). The paper, titled “A Hybrid Explainable Model Based on Advanced Machine Learning and Deep Learning Models for Classifying Brain Tumors Using MRI Images,” introduces an innovative AI framework for accurate brain tumor classification using MRI scans.
This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE).
A lightweight PDSCNN is then employed to extract relevant tumor-specific patterns while minimizing computational complexity. A hybrid RRELM model is proposed, enhancing the traditional ELM for improved classification performance. The proposed framework is compared with various state-of-the-art models in terms of classification accuracy, model parameters, and layer sizes.
The introduction of ridge regression in the ELM framework led to significant enhancements in classification performance model parameters and layer sizes compared to those of the state-of-the-art models.
This research, a collaboration between Cihan University Sulaimaniya and institutions from Bangladesh, Qatar, the UK, Australia, and Poland, showcases the power of global partnerships in advancing medical AI applications. It underscores the university’s commitment to impactful research and innovation.
Read the full article here:
https://www.nature.com/articles/s41598-025-85874-7.
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