Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

Xiaohui Yang, Kun Zhang^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1843-1859, 2024, DOI:10.32604/cmc.2023.047660

Abstract Data is regarded as a valuable asset, and sharing data is a prerequisite for fully exploiting the value of data. However, the current medical data sharing scheme lacks a fair incentive mechanism, and the authenticity of data cannot be guaranteed, resulting in low enthusiasm of participants. A fair and trusted medical data trading scheme based on smart contracts is proposed, which aims to encourage participants to be honest and improve their enthusiasm for participation. The scheme uses zero-knowledge range proof for trusted verification, verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction,… More >

Mehdi Hassan¹, Shuanghu Fang^1,*, Muhammad Rizwan², Asma Seemi Malik³, Iqra Mushtaque⁴

International Journal of Mental Health Promotion, Vol.26, No.1, pp. 1-9, 2024, DOI:10.32604/ijmhp.2023.043096

Abstract This study examined the effects of financial stress, parental expectation and test anxiety on suicidal ideation in pre-medical students. For this purpose, a cross-sectional research design was used, and data were collected through a non-probability sampling technique. The sample consisted of 425 pre-medical students. Our results indicate a strong and positive association between parental expectation and suicidal ideation (β = 0.272; t = 3.573; p < 0.000). Likewise, entrance test exam anxiety has a positive association with suicidal ideation among pre-medical students (β = 0.394; t = 3.933; p < 0.000). Lastly, there is a significant and positive association between… More >

Mohamed Abdel-Basset¹, Hossam Hawash¹, Mohamed Abouhawwash^2,3,*, S. S. Askar⁴, Alshaimaa A. Tantawy¹

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1171-1187, 2024, DOI:10.32604/cmc.2023.044425

Abstract The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans. This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis. This paper proposes a novel deep learning approach, called Conformer Network, for explainable discrimination of viral pneumonia depending on the lung Region of Infections (ROI) within a single modality radiographic CT scan. Firstly, an efficient U-shaped transformer network is integrated for lung image segmentation. Then, a robust transfer learning technique is introduced to design a robust feature… More >

Lu Chen^1,#, Huaqiang Chen^2,#, Zhikai Pan⁷, Sheng Xu², Guangsheng Lai², Shuwen Chen^2,5,6, Shuihua Wang^3,8, Xiaodong Gu^2,6,*, Yudong Zhang^3,4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 361-382, 2024, DOI:10.32604/cmes.2023.031229

Abstract Aim: This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately. Methods: A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model’s generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit… More >

Muwei Jian^1,2,*, Yue Jin¹, Hui Yu³

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 3051-3063, 2024, DOI:10.32604/cmes.2023.030236

Abstract Universal lesion detection (ULD) methods for computed tomography (CT) images play a vital role in the modern clinical medicine and intelligent automation. It is well known that single 2D CT slices lack spatial-temporal characteristics and contextual information compared to 3D CT blocks. However, 3D CT blocks necessitate significantly higher hardware resources during the learning phase. Therefore, efficiently exploiting temporal correlation and spatial-temporal features of 2D CT slices is crucial for ULD tasks. In this paper, we propose a ULD network with the enhanced temporal correlation for this purpose, named TCE-Net. The designed TCE module is applied to enrich the discriminate… More >

Liwei Deng¹, Henan Sun¹, Jing Wang², Sijuan Huang³, Xin Yang^3,*

CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2271-2287, 2023, DOI:10.32604/cmc.2023.039062

Abstract In recent years, radiotherapy based only on Magnetic Resonance (MR) images has become a hot spot for radiotherapy planning research in the current medical field. However, functional computed tomography (CT) is still needed for dose calculation in the clinic. Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest, making radiotherapy based only on MR images possible. In this paper, we proposed a novel unsupervised image synthesis framework with registration networks. This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image… More >

Shazia Shamas¹, Surya Narayan Panda^1,*, Ishu Sharma^1,*, Kalpna Guleria¹, Aman Singh^2,3,4, Ahmad Ali AlZubi⁵, Mallak Ahmad AlZubi⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1051-1075, 2024, DOI:10.32604/cmes.2023.030712

Abstract The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis and planning intervention. This research work addresses the major issues pertaining to the field of medical image processing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposes an improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. The better resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In this process, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarm intelligent techniques. The techniques… More >

Huzaira Razzaque¹, Shahzaib Ashraf^1,*, Muhammad Naeem², Yu-Ming Chu^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1925-1950, 2024, DOI:10.32604/cmes.2023.030030

Abstract Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) proved more effective for handling uncertainty and vagueness in multi-criteria decision-making (MADM). It does not only cover the data in two variable parameters but is also beneficial for three parametric data. By Pythagorean fuzzy sets, the difference is calculated only between two parameters (membership and non-membership). According to human thoughts, fuzzy data can be found in three parameters (membership uncertainty, and non-membership). So, to make a compromise decision, comparing Sq-LDFSs is essential. Existing measures of different fuzzy sets do, however, can have several flaws that can lead to counterintuitive results. For instance, they treat… More >

Eman S. Attia¹, Ashraf A. M. Khalaf¹, Fathi E. Abd El-Samie², Saied M. Abd El-atty^2,*, Konstantinos A. Lizos^3,#, Osama Alfarraj⁴, Heba M. El-Hoseny⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1259-1275, 2024, DOI:10.32604/cmes.2023.028990

Abstract Recently, nano-systems based on molecular communications via diffusion (MCvD) have been implemented in a variety of nanomedical applications, most notably in targeted drug delivery system (TDDS) scenarios. Furthermore, because the MCvD is unreliable and there exists molecular noise and inter symbol interference (ISI), cooperative nano-relays can acquire the reliability for drug delivery to targeted diseased cells, especially if the separation distance between the nano transmitter and nano receiver is increased. In this work, we propose an approach for optimizing the performance of the nano system using cooperative molecular communications with a nano relay scheme, while accounting for blood flow effects… More >