Mohammed Alshahrani¹, Mohammed Al-Jabbar^1,*, Ebrahim Mohammed Senan^2,3, Fatima Ali Amer jid Almahri⁴, Sultan Ahmed Almalki¹, Eman A. Alshari^3,5

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3639-3675, 2025, DOI:10.32604/cmes.2025.064668 - 30 June 2025

Abstract Multiple Sclerosis (MS) poses significant health risks. Patients may face neurodegeneration, mobility issues, cognitive decline, and a reduced quality of life. Manual diagnosis by neurologists is prone to limitations, making AI-based classification crucial for early detection. Therefore, automated classification using Artificial Intelligence (AI) techniques has a crucial role in addressing the limitations of manual classification and preventing the development of MS to advanced stages. This study developed hybrid systems integrating XGBoost (eXtreme Gradient Boosting) with multi-CNN (Convolutional Neural Networks) features based on Ant Colony Optimization (ACO) and Maximum Entropy Score-based Selection (MESbS) algorithms for early… More >

Smita Khairnar^1,2, Shilpa Gite^1,3,*, Biswajeet Pradhan^4,*, Sudeep D. Thepade^2,5, Abdullah Alamri⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3677-3707, 2025, DOI:10.32604/cmes.2025.058855 - 30 June 2025

Abstract Face liveness detection is essential for securing biometric authentication systems against spoofing attacks, including printed photos, replay videos, and 3D masks. This study systematically evaluates pre-trained CNN models— DenseNet201, VGG16, InceptionV3, ResNet50, VGG19, MobileNetV2, Xception, and InceptionResNetV2—leveraging transfer learning and fine-tuning to enhance liveness detection performance. The models were trained and tested on NUAA and Replay-Attack datasets, with cross-dataset generalization validated on SiW-MV2 to assess real-world adaptability. Performance was evaluated using accuracy, precision, recall, FAR, FRR, HTER, and specialized spoof detection metrics (APCER, NPCER, ACER). Fine-tuning significantly improved detection accuracy, with DenseNet201 achieving the highest… More > Graphic Abstract

Xin Wang^*, Shuang Liu, Chang-Cai Zhou

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 221-234, 2023, DOI:10.32604/iasc.2023.036766 - 29 April 2023

Abstract Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then… More >

Md. Saiful Islam, Shuvo Jyoti Das, Md. Riajul Alam Khan, Sifat Momen^*, Nabeel Mohammed

Computer Systems Science and Engineering, Vol.44, No.1, pp. 519-534, 2023, DOI:10.32604/csse.2023.025282 - 01 June 2022

Abstract COVID-19 has created a panic all around the globe. It is a contagious disease caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), originated from Wuhan in December 2019 and spread quickly all over the world. The healthcare sector of the world is facing great challenges tackling COVID cases. One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases. In this article, we propose a deep Convolutional Neural Network (CNN) based approach to detect COVID+ (i.e., patients with COVID-19), pneumonia and normal cases, from the… More >