Ghadah Naif Alwakid^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.068666 - 10 November 2025

Abstract Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that significantly affects cognitive function, making early and accurate diagnosis essential. Traditional Deep Learning (DL)-based approaches often struggle with low-contrast MRI images, class imbalance, and suboptimal feature extraction. This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans. Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN). A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient (MCC)-based evaluation method into the design.… More >

Christine Dewi^1,2, Melati Viaeritas Vitrieco Santoso¹, Hanna Prillysca Chernovita³, Evangs Mailoa¹, Stephen Abednego Philemon¹, Abbott Po Shun Chen^4,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5361-5379, 2025, DOI:10.32604/cmc.2025.067381 - 30 July 2025

Abstract Early detection of Forest and Land Fires (FLF) is essential to prevent the rapid spread of fire as well as minimize environmental damage. However, accurate detection under real-world conditions, such as low light, haze, and complex backgrounds, remains a challenge for computer vision systems. This study evaluates the impact of three image enhancement techniques—Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and a hybrid method called DBST-LCM CLAHE—on the performance of the YOLOv11 object detection model in identifying fires and smoke. The D-Fire dataset, consisting of 21,527 annotated images captured under diverse environmental scenarios… More >

Deepthi K. Oommen^*, J. Arunnehru

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640 - 08 June 2023

Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the… More >

K. Jagadeesh^1,*, A. Rajendran²

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2017-2032, 2023, DOI:10.32604/csse.2023.029169 - 03 November 2022

Abstract Lung Cancer is one of the hazardous diseases that have to be detected in earlier stages for providing better treatment and clinical support to patients. For lung cancer diagnosis, the computed tomography (CT) scan images are to be processed with image processing techniques and effective classification process is required for appropriate cancer diagnosis. In present scenario of medical data processing, the cancer detection process is very time consuming and exactitude. For that, this paper develops an improved model for lung cancer segmentation and classification using genetic algorithm. In the model, the input CT images are More >

P. Indra, G. Kavithaa^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 877-893, 2022, DOI:10.32604/iasc.2022.025982 - 03 May 2022

Abstract Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases’ quality. In Medical Images, detection of breast cancer in its earlier stage is most important in this field. Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists. Hence, improvement of computer-supported strategies has been introduced for breast cancer identification. This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network… More >

D. Devina Merin^1,*, P. Jagatheeswari²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1849-1860, 2022, DOI:10.32604/iasc.2022.024495 - 24 March 2022

Abstract The Deep learning (DL) network is an effective technique that has extended application in medicine, robotics, biotechnology, biometrics and communication. The unique architecture of DL networks can be trained according to classify any complex tasks in a limited duration. In the proposed work a deep convolution neural network of DL is trained to classify the antimicrobial activity of silver nanoparticles (AgNP). The process involves two processing steps; synthesis of silver nanoparticles and classification (SEM) of AgNP based on the antimicrobial activity. AgNP images from scanning electron microscope are pre-processed using Adaptive Histogram Equalization in the More >

Lord Amoah^1,2,*, Ampofo Twumasi Kwabena³

Journal of Quantum Computing, Vol.3, No.2, pp. 65-77, 2021, DOI:10.32604/jqc.2021.020734 - 22 June 2021

Abstract Recent contrast enhancement (CE) methods, with a few exceptions, predominantly focus on enhancing gray-scale images. This paper proposes a bihistogram shifting contrast enhancement for color images based on the RGB (red, green, and blue) color model. The proposed method selects the two highest bins and two lowest bins from the image histogram, performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images. The proposed method simultaneously performs both right histogram shifting (RHS) and left histogram shifting (LHS) in each histogram shifting repetition to embed and… More >

Ahmet Elbir^1,∗, Hamza Osman Ilhan¹, Nizamettin Aydin¹

Computer Systems Science and Engineering, Vol.34, No.2, pp. 101-107, 2019, DOI:10.32604/csse.2019.34.101

Abstract The performances of the multivariate techniques are directly related to the variable selection process, which is time consuming and requires resources for testing each possible parameter to achieve the best results. Therefore, optimization methods for variable selection process have been proposed in the literature to find the optimal solution in short time by using less system resources. Contrast enhancement is the one of the most important and the parameter dependent image enhancement technique. In this study, two optimization methods are employed for the variable selection for the contrast enhancement technique. Particle swarm optimization (PSO) and More >