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 >

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 >

Nojood O Aljehane^*

Computer Systems Science and Engineering, Vol.47, No.3, pp. 3109-3126, 2023, DOI:10.32604/csse.2023.038042

Abstract Medical image analysis is an active research topic, with thousands of studies published in the past few years. Transfer learning (TL) including convolutional neural networks (CNNs) focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance. It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time. This study develops an Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System (ETSOTL-MIAS). The goal of the ETSOTL-MIAS technique lies in the identification and classification of… More >

M. Muthalakshmi^1,*, T. Merlin Inbamalar², C. Chandravathi³, K. Saravanan⁴

Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449

Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling… More >

Fuat Türk^*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1357-1373, 2023, DOI:10.32604/csse.2023.030772

Abstract Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the… More >

Hong’an Li¹, Min Zhang^1,*, Dufeng Chen², Jing Zhang¹, Meng Yang³, Zhanli Li¹

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369

Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering… More > Graphic Abstract

Vicky Mudeng^1,2, Jin-woo Jeong³, Se-woon Choe^1,4,*

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4677-4693, 2022, DOI:10.32604/cmc.2022.031046

Abstract A lump growing in the breast may be referred to as a breast mass related to the tumor. However, not all tumors are cancerous or malignant. Breast masses can cause discomfort and pain, depending on the size and texture of the breast. With an appropriate diagnosis, non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant. With the development of the artificial neural network, the deep discriminative model, such as a convolutional neural network, may evaluate the breast lesion to distinguish benign and malignant cancers from mammogram breast masses images. This work accomplished breast masses classification… More >

Mustafa Musa Jaber^1,2,*, Salman Yussof¹, Amer S. Elameer³, Leong Yeng Weng¹, Sura Khalil Abd^2,6, Anand Nayyar^4,5

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2175-2190, 2022, DOI:10.32604/cmc.2022.023387

Abstract Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic… More >

B. Priestly Shan¹, O. Jeba Shiney¹, Sharzeel Saleem², V. Rajinikanth³, Atef Zaguia⁴, Dilbag Singh^5,*

CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057

Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two… More >

Yankun Cao¹, Lina Xu³, Zhi Liu², Xiaoyan Xiao⁴, Mingyu Wang⁵, Qin Li⁶, Hongji Xu², Geng Yang^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 851-863, 2022, DOI:10.32604/cmes.2022.018424

Abstract Contribution: This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology. The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system, and strengthen the teaching practice of deep learning related courses in hospitals, so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis. Background: In recent years, deep learning has been widely used in academia, industry, and medicine. An increasing number of companies are starting to recruit a large number of professionals in the… More >