Muhammad Javaid Iqbal¹, Muhammad Waseem Iqbal², Muhammad Anwar^3,*, Muhammad Murad Khan⁴, Abd Jabar Nazimi⁵, Mohammad Nazir Ahmad⁶

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5267-5281, 2023, DOI:10.32604/cmc.2023.033024

Abstract The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images simultaneously, and many errors occurred… More >

Sureshkumar Rajagopal¹, Tamilvizhi Thanarajan^2,*, Youseef Alotaibi³, Saleh Alghamdi⁴

Computer Systems Science and Engineering, Vol.45, No.2, pp. 2093-2109, 2023, DOI:10.32604/csse.2023.032488

Abstract Automated segmentation of brain tumors using Magnetic Resonance Imaging (MRI) data is critical in the analysis and monitoring of disease development. As a result, gliomas are aggressive and diverse tumors that may be split into intra-tumoral groups by using effective and accurate segmentation methods. It is intended to extract characteristics from an image using the Gray Level Co-occurrence (GLC) matrix feature extraction method described in the proposed work. Using Convolutional Neural Networks (CNNs), which are commonly used in biomedical image segmentation, CNNs have significantly improved the precision of the state-of-the-art segmentation of a brain tumor. Using two segmentation networks, a… More >

Mostafa Elbaz^1,2,*, Haitham Elwahsh¹, Ibrahim Mahmoud El-Henawy²

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2927-2944, 2023, DOI:10.32604/cmc.2023.033111

Abstract Computer vision is one of the significant trends in computer science. It plays as a vital role in many applications, especially in the medical field. Early detection and segmentation of different tumors is a big challenge in the medical world. The proposed framework uses ultrasound images from Kaggle, applying five diverse models to denoise the images, using the best possible noise-free image as input to the U-Net model for segmentation of the tumor, and then using the Convolution Neural Network (CNN) model to classify whether the tumor is benign, malignant, or normal. The main challenge faced by the framework in… More >

R. Poonguzhali¹, Sultan Ahmad², P. Thiruvannamalai Sivasankar³, S. Anantha Babu³, Pranav Joshi⁴, Gyanendra Prasad Joshi⁵, Sung Won Kim^6,*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2179-2194, 2023, DOI:10.32604/cmc.2023.032816

Abstract Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented ADRU-SCM model majorly focuses on… More >

Rehana Ghulam¹, Sammar Fatima¹, Tariq Ali¹, Nazir Ahmad Zafar¹, Abdullah A. Asiri², Hassan A. Alshamrani^2,*, Samar M. Alqhtani³, Khlood M. Mehdar⁴

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1333-1349, 2023, DOI:10.32604/cmc.2023.031695

Abstract Human brain consists of millions of cells to control the overall structure of the human body. When these cells start behaving abnormally, then brain tumors occurred. Precise and initial stage brain tumor detection has always been an issue in the field of medicines for medical experts. To handle this issue, various deep learning techniques for brain tumor detection and segmentation techniques have been developed, which worked on different datasets to obtain fruitful results, but the problem still exists for the initial stage of detection of brain tumors to save human lives. For this purpose, we proposed a novel U-Net-based Convolutional… More >

Wenbin Wu¹, Guanjun Liu^1,*, Kaiyi Liang², Hui Zhou²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1323-1335, 2023, DOI:10.32604/cmes.2022.020428

Abstract Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction. U-Net has been the baseline model since the very beginning due to a symmetrical U-structure for better feature extraction and fusing and suitable for small datasets. To enhance the segmentation performance of U-Net, cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine. However, the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the… More >

Mu Ye^1,2,3,4, Li Ji¹, Luo Tianye¹, Li Sihan⁵, Zhang Tong¹, Feng Ruilong¹, Hu Tianli^1,2,3,4, Gong He^1,2,3,4, Guo Ying^1,2,3,4, Sun Yu^1,2,3,4, Thobela Louis Tyasi⁶, Li Shijun^7,8,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 6195-6205, 2022, DOI:10.32604/cmc.2022.026880

Abstract Remote sensing image analysis is a basic and practical research hotspot in remote sensing science. Remote sensing images contain abundant ground object information and it can be used in urban planning, agricultural monitoring, ecological services, geological exploration and other aspects. In this paper, we propose a lightweight model combining vgg-16 and u-net network. By combining two convolutional neural networks, we classify scenes of remote sensing images. While ensuring the accuracy of the model, try to reduce the memory of the model. According to the experimental results of this paper, we have improved the accuracy of the model to 98%. The… More >

Kazim Ali^1,*, Adnan N. Qureshi¹, Muhammad Shahid Bhatti², Abid Sohail², Mohammad Hijji³

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2237-2256, 2023, DOI:10.32604/iasc.2023.028810

Abstract Deep learning-based systems have succeeded in many computer vision tasks. However, it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks. These attacks can quickly spoil deep learning models, e.g., different convolutional neural networks (CNNs), used in various computer vision tasks from image classification to object detection. The adversarial examples are carefully designed by injecting a slight perturbation into the clean images. The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense, Generative Adversarial Network Defense, Deep Regret Analytic Generative Adversarial Networks Defense, Deep Denoising… More >

Adel Hafeezallah¹, Ahlam Al-Dhamari^2,3,*, Syed Abd Rahman Abu-Bakar²

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3879-3903, 2022, DOI:10.32604/cmc.2022.028289

Abstract Estimating the crowd count and density of highly dense scenes witnessed in Muslim gatherings at religious sites in Makkah and Madinah is critical for developing control strategies and organizing such a large gathering. Moreover, since the crowd images in this case can range from low density to high density, detection-based approaches are hard to apply for crowd counting. Recently, deep learning-based regression has become the prominent approach for crowd counting problems, where a density-map is estimated, and its integral is further computed to acquire the final count result. In this paper, we put forward a novel multi-scale network (named 2U-Net)… More >

Ni Ruiwen¹, Mu Ye^1,2,3,4,*, Li Ji¹, Zhang Tong¹, Luo Tianye¹, Feng Ruilong¹, Gong He^1,2,3,4, Hu Tianli^1,2,3,4, Sun Yu^1,2,3,4, Guo Ying^1,2,3,4, Li Shijun^5,6, Thobela Louis Tyasi⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3263-3274, 2022, DOI:10.32604/cmc.2022.026881

Abstract In order to accurately segment architectural features in high-resolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed. First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building. The remote sensing image and its truth map were used as the input in the U-net network, followed by the addition of the building ground prediction layer at the end of the U-net network. Based on the ResNet network, a multi-task network with the boundary distance prediction layer was built. Experiments involving the ISPRS aerial… More >