Joint Scientific Research Published by a Faculty Member and a Researcher from the College of Engineering, University of Basrah, in a Prestigious International Journal

Professor Ghaidaa Abdul-Razzaq Al-Suhail, a faculty member in the Department of Computer Engineering at the College of Engineering, University of Basrah, in collaboration with researcher Noor Saad Hanoun—a postgraduate student—has published a scientific paper in the Swiss journal Applied System Innovation (ASI). This reputable journal is published by the international academic publisher MDPI and is indexed in major global databases, ranking in the first quartile (Q1) in Scopus (with an impact factor of 9.0) and the second quartile (Q2) in Clarivate (with an impact factor of 3.4). The paper was published in Volume 8, Issue 5, in 2025.
Title:
LCxNet: An Explainable CNN Framework for Lung Cancer Detection in CT Images Using Multi-Optimizer and Visual Interpretability

Abstract
Lung cancer, the leading cause of cancer-related mortality worldwide, necessitates better methods for earlier and more accurate detection. To this end, this study introduces LCxNet, a novel, custom-designed convolutional neural network (CNN) framework for computer-aided diagnosis (CAD) of lung cancer. The IQ-OTH/NCCD lung CT dataset, which includes three different classes—benign, malignant, and normal—is used to train and assess the model. The framework is implemented using five optimizers, SGD, RMSProp, Adam, AdamW, and NAdam, to compare the learning behavior and performance stability. To bridge the gap between model complexity and clinical utility, we integrated Explainable AI (XAI) methods, specifically Grad-CAM for decision visualization and t-SNE for feature space analysis. With accuracy, specificity, and AUC values of 99.39%, 99.45%, and 100%, respectively, the results demonstrate that the LCxNet model outperformed the state-of-the-art models in terms of diagnostic performance. In conclusion, this study emphasizes how crucial XAI is to creating trustworthy and efficient clinical tools for the early detection of lung cancer.
Keywords: lung cancer detection; CAD; CNN; CT Scans; optimizers; Grad-CAM; t-SNE; XA

http://https://www.mdpi.com/2571-5577/8/5/153