Nameer Baht^1,*, Enrique Domínguez^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4441-4455, 2025, DOI:10.32604/cmc.2025.069528 - 23 October 2025

Abstract Coffee is an important agricultural commodity, and its production is threatened by various diseases. It is also a source of concern for coffee-exporting countries, which is causing them to rethink their strategies for the future. Maintaining crop production requires early diagnosis. Notably, Coffee Leaf Miner (CLM) Machine learning (ML) offers promising tools for automated disease detection. Early detection of CLM is crucial for minimising yield losses. However, this study explores the effectiveness of using Convolutional Neural Networks (CNNs) with transfer learning algorithms ResNet50, DenseNet121, MobileNet, Inception, and hybrid VGG19 for classifying coffee leaf images as… More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4161-4179, 2025, DOI:10.32604/cmc.2025.061497 - 06 March 2025

Abstract A healthy brain is vital to every person since the brain controls every movement and emotion. Sometimes, some brain cells grow unexpectedly to be uncontrollable and cancerous. These cancerous cells are called brain tumors. For diagnosed patients, their lives depend mainly on the early diagnosis of these tumors to provide suitable treatment plans. Nowadays, Physicians and radiologists rely on Magnetic Resonance Imaging (MRI) pictures for their clinical evaluations of brain tumors. These evaluations are time-consuming, expensive, and require expertise with high skills to provide an accurate diagnosis. Scholars and industrials have recently partnered to implement… More >

Saad Akbar^1,2, Humera Azam¹, Sulaiman Sulmi Almutairi^3,*, Omar Alqahtani⁴, Habib Shah⁴, Aliya Aleryani⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1075-1104, 2024, DOI:10.32604/cmc.2024.050913 - 18 July 2024

Abstract The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic tools. In this article, a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19, pneumonia, and normal conditions in chest X-ray images (CXIs) is proposed coupled with Explainable Artificial Intelligence (XAI). Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3, VGG16, and VGG19 that excel in the task of feature extraction. The methodology is further enhanced by the inclusion of the t-SNE (t-Distributed… More >

S. Deva Kumar, S. Venkatramaphanikumar^*, K. Venkata Krishna Kishore

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165 - 29 April 2023

Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge… More >

Abdullah A. Asiri¹, Muhammad Aamir², Ahmad Shaf^2,*, Tariq Ali², Muhammad Zeeshan³, Muhammad Irfan⁴, Khalaf A. Alshamrani¹, Hassan A. Alshamrani¹, Fawaz F. Alqahtani¹, Ali H. D. Alshehri¹

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5735-5753, 2022, DOI:10.32604/cmc.2022.031747 - 28 July 2022

Abstract The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients. Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images (MRIs) created in medical practice is a problematic and timewasting task for experts. As a result, there is a critical necessity for more accurate computer-aided methods for early tumor detection. To remove this gap, we enhanced the computational power of a computer-aided system by proposing a fine-tuned Block-Wise Visual Geometry Group19 (BW-VGG19) architecture. In this method, a pre-trained VGG19 is fine-tuned with CNN More >

Pierre-Frédéric Villard^1,*, Maureen Boudart², Ioana Ilea³, Fabien Pierre¹

FDMP-Fluid Dynamics & Materials Processing, Vol.18, No.6, pp. 1639-1648, 2022, DOI:10.32604/fdmp.2022.021726 - 27 June 2022

Abstract Industrial woven meshes are composed of metal materials and are often used in construction, industrial and residential activities or applications. The objective of this work is defect detection in industrial fabrics in the quality control stage. In order to overcome the limitations of manual methods, which are often tedious and time-consuming, we propose a strategy that can automatically detect defects in micrometric steel meshes by means of a Convolutional Neural Network. The database used for such a purpose comes from real problem data for anomaly detection in micrometric woven meshes. This detection is performed through More >

Saleh Albahli^1,2,*, Ghulam Nabi Ahmad Hassan Yar³

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 837-853, 2022, DOI:10.32604/iasc.2022.024427 - 08 February 2022

Abstract Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of… More >