Yunjing Liu^1,, Erhu Zhang^1,2,,*, Jingjing Wang³, Guangfeng Lin², Jinghong Duan⁴

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1633-1653, 2024, DOI:10.32604/cmc.2024.052611

Abstract Recovering high-quality inscription images from unknown and complex inscription noisy images is a challenging research issue. Different from natural images, character images pay more attention to stroke information. However, existing models mainly consider pixel-level information while ignoring structural information of the character, such as its edge and glyph, resulting in reconstructed images with mottled local structure and character damage. To solve these problems, we propose a novel generative adversarial network (GAN) framework based on an edge-guided generator and a discriminator constructed by a dual-domain U-Net framework, i.e., EDU-GAN. Unlike existing frameworks, the generator introduces the… More >

Roseline Oluwaseun Ogundokun^1,2, Joseph Bamidele Awotunde³, Hakeem Babalola Akande⁴, Cheng-Chi Lee^5,6,*, Agbotiname Lucky Imoize^7,8

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 139-161, 2024, DOI:10.32604/cmc.2024.052153

Abstract Mobile technology is developing significantly. Mobile phone technologies have been integrated into the healthcare industry to help medical practitioners. Typically, computer vision models focus on image detection and classification issues. MobileNetV2 is a computer vision model that performs well on mobile devices, but it requires cloud services to process biometric image information and provide predictions to users. This leads to increased latency. Processing biometrics image datasets on mobile devices will make the prediction faster, but mobiles are resource-restricted devices in terms of storage, power, and computational speed. Hence, a model that is small in size,… More >

Anas AbuKaraki¹, Tawfi Alrawashdeh¹, Sumaya Abusaleh¹, Malek Zakarya Alksasbeh^1,*, Bilal Alqudah¹, Khalid Alemerien², Hamzah Alshamaseen³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420

Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

Haiyue Li¹, Jing Xie², Jing Ke³, Ye Yuan¹, Xiaoyong Pan¹, Hongyi Xin⁴, Hongbin Shen^1,*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 487-506, 2024, DOI:10.32604/cmc.2024.049640

Abstract Multi-modal histological image registration tasks pose significant challenges due to tissue staining operations causing partial loss and folding of tissue. Convolutional neural network (CNN) and generative adversarial network (GAN) are pivotal in medical image registration. However, existing methods often struggle with severe interference and deformation, as seen in histological images of conditions like Cushing’s disease. We argue that the failure of current approaches lies in underutilizing the feature extraction capability of the discriminator in GAN. In this study, we propose a novel multi-modal registration approach GAN-DIRNet based on GAN for deformable histological image registration. To… More >

Mian Muhammad Danyal^1,2, Sarwar Shah Khan^3,4,*, Rahim Shah Khan⁵, Saifullah Jan², Naeem ur Rahman⁶

Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340

Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

Md. Atiqur Rahman¹, Mustavi Ibne Masum¹, Khan Md Hasib², M. F. Mridha^3,*, Sultan Alfarhood⁴, Mejdl Safran^4,*, Dunren Che⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2425-2448, 2024, DOI:10.32604/cmes.2024.050760

Abstract Brain tumors pose a significant threat to human lives and have gained increasing attention as the tenth leading cause of global mortality. This study addresses the pressing issue of brain tumor classification using Magnetic resonance imaging (MRI). It focuses on distinguishing between Low-Grade Gliomas (LGG) and High-Grade Gliomas (HGG). LGGs are benign and typically manageable with surgical resection, while HGGs are malignant and more aggressive. The research introduces an innovative custom convolutional neural network (CNN) model, Glioma-CNN. GliomaCNN stands out as a lightweight CNN model compared to its predecessors. The research utilized the BraTS 2020 More >

Shiwen Song, Rui Zhang, Min Hu^*, Feiyao Huang

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5243-5271, 2024, DOI:10.32604/cmc.2024.050879

Abstract Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security. Currently, with the emergence of massive high-resolution multi-modality images, the use of multi-modality images for fine-grained recognition has become a promising technology. Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples. The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features. The attention mechanism helps the model to pinpoint the key information in the image, resulting in a… More >

Yu Zuo¹, Wenwen Li^2,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4413-4431, 2024, DOI:10.32604/cmc.2024.049805

Abstract In cornfields, factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation. In addition, remote areas such as farmland are usually constrained by limited computational resources and limited collected data. Therefore, it becomes necessary to lighten the model to better adapt to complex cornfield scene, and make full use of the limited data information. In this paper, we propose an improved image segmentation algorithm based on unet. Firstly, the inverted residual structure is introduced into the contraction path to reduce the… More >

Peng Xiao¹, Qi Zhong², Jingxue Chen¹, Dongyuan Wu¹, Zhen Qin¹, Erqiang Zhou^1,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4703-4724, 2024, DOI:10.32604/cmc.2024.049791

Abstract In the intelligent medical diagnosis area, Artificial Intelligence (AI)’s trustworthiness, reliability, and interpretability are critical, especially in cancer diagnosis. Traditional neural networks, while excellent at processing natural images, often lack interpretability and adaptability when processing high-resolution digital pathological images. This limitation is particularly evident in pathological diagnosis, which is the gold standard of cancer diagnosis and relies on a pathologist’s careful examination and analysis of digital pathological slides to identify the features and progression of the disease. Therefore, the integration of interpretable AI into smart medical diagnosis is not only an inevitable technological trend but… More >

Ahmed H. Alhadethi^1,*, Ikram Smaoui², Ahmed Fakhfakh³, Saad M. Darwish⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4825-4844, 2024, DOI:10.32604/cmc.2024.047852

Abstract The act of transmitting photos via the Internet has become a routine and significant activity. Enhancing the security measures to safeguard these images from counterfeiting and modifications is a critical domain that can still be further enhanced. This study presents a system that employs a range of approaches and algorithms to ensure the security of transmitted venous images. The main goal of this work is to create a very effective system for compressing individual biometrics in order to improve the overall accuracy and security of digital photographs by means of image compression. This paper introduces… More >