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Search Results (20)
  • Open Access

    REVIEW

    Circular RNAs in breast cancer diagnosis, treatment and prognosis

    XIAOJIA HUANG1,#, CAILU SONG2,#, JINHUI ZHANG2, LEWEI ZHU3,*, HAILIN TANG2,*

    Oncology Research, Vol.32, No.2, pp. 241-249, 2024, DOI:10.32604/or.2023.046582

    Abstract Breast cancer has surpassed lung cancer to become the most common malignancy worldwide. The incidence rate and mortality rate of breast cancer continue to rise, which leads to a great burden on public health. Circular RNAs (circRNAs), a new class of noncoding RNAs (ncRNAs), have been recognized as important oncogenes or suppressors in regulating cancer initiation and progression. In breast cancer, circRNAs have significant roles in tumorigenesis, recurrence and multidrug resistance that are mediated by various mechanisms. Therefore, circRNAs may serve as promising targets of therapeutic strategies for breast cancer management. This study reviews the most recent studies about the… More >

  • Open Access

    ARTICLE

    Enhancing Breast Cancer Diagnosis with Channel-Wise Attention Mechanisms in Deep Learning

    Muhammad Mumtaz Ali, Faiqa Maqsood, Shiqi Liu, Weiyan Hou, Liying Zhang, Zhenfei Wang*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 2699-2714, 2023, DOI:10.32604/cmc.2023.045310

    Abstract Breast cancer, particularly Invasive Ductal Carcinoma (IDC), is a primary global health concern predominantly affecting women. Early and precise diagnosis is crucial for effective treatment planning. Several AI-based techniques for IDC-level classification have been proposed in recent years. Processing speed, memory size, and accuracy can still be improved for better performance. Our study presents ECAM, an Enhanced Channel-Wise Attention Mechanism, using deep learning to analyze histopathological images of Breast Invasive Ductal Carcinoma (BIDC). The main objectives of our study are to enhance computational efficiency using a Separable CNN architecture, improve data representation through hierarchical feature aggregation, and increase accuracy and… More >

  • Open Access

    ARTICLE

    Smart MobiNet: A Deep Learning Approach for Accurate Skin Cancer Diagnosis

    Muhammad Suleman1, Faizan Ullah1, Ghadah Aldehim2,*, Dilawar Shah1, Mohammad Abrar1,3, Asma Irshad4, Sarra Ayouni2

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3533-3549, 2023, DOI:10.32604/cmc.2023.042365

    Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from the International Skin Imaging Collaboration,… More >

  • Open Access

    ARTICLE

    SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

    Jiaji Wang1, Muhammad Attique Khan2, Shuihua Wang1,3, Yudong Zhang1,3,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2201-2216, 2023, DOI:10.32604/cmc.2023.041191

    Abstract Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train a support vector machine (SVM)… More >

  • Open Access

    REVIEW

    Circulating tumor cells and circulating tumor DNA in breast cancer diagnosis and monitoring

    EFFAT ALEMZADEH1, LEILA ALLAHQOLI2, HAMIDEH DEHGHAN3, AFROOZ MAZIDIMORADI4, ALIREZA GHASEMPOUR3, HAMID SALEHINIYA5,*

    Oncology Research, Vol.31, No.5, pp. 667-675, 2023, DOI:10.32604/or.2023.028406

    Abstract Liquid biopsy, including both circulating tumor cells and circulating tumor DNA, is becoming more popular as a diagnostic tool in the clinical management of breast cancer. Elevated concentrations of these biomarkers during cancer treatment may be used as markers for cancer progression as well as to understand the mechanisms underlying metastasis and treatment resistance. Thus, these circulating markers serve as tools for cancer assessing and monitoring through a simple, non-invasive blood draw. However, despite several study results currently noting a potential clinical impact of ctDNA mutation tracking, the method is not used clinically in cancer diagnosis among patients and more… More > Graphic Abstract

    Circulating tumor cells and circulating tumor DNA in breast cancer diagnosis and monitoring

  • Open Access

    ARTICLE

    Breast Cancer Diagnosis Using Artificial Intelligence Approaches: A Systematic Literature Review

    Alia Alshehri, Duaa AlSaeed*

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 939-970, 2023, DOI:10.32604/iasc.2023.037096

    Abstract One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the… More >

  • Open Access

    ARTICLE

    An Intelligent Decision Support System for Lung Cancer Diagnosis

    Ahmed A. Alsheikhy1,*, Yahia F. Said1, Tawfeeq Shawly2

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 799-817, 2023, DOI:10.32604/csse.2023.035269

    Abstract Lung cancer is the leading cause of cancer-related death around the globe. The treatment and survival rates among lung cancer patients are significantly impacted by early diagnosis. Most diagnostic techniques can identify and classify only one type of lung cancer. It is crucial to close this gap with a system that detects all lung cancer types. This paper proposes an intelligent decision support system for this purpose. This system aims to support the quick and early detection and classification of all lung cancer types and subtypes to improve treatment and save lives. Its algorithm uses a Convolutional Neural Network (CNN)… More >

  • Open Access

    ARTICLE

    Auxiliary Classifier of Generative Adversarial Network for Lung Cancer Diagnosis

    P. S. Ramapraba1,*, P. Epsiba2, K. Umapathy3, E. Sivanantham4

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2177-2189, 2023, DOI:10.32604/iasc.2023.032040

    Abstract The classification of lung nodules is a challenging problem as the visual analysis of the nodules and non-nodules revealed homogenous textural patterns. In this work, an Auxiliary Classifier (AC)-Generative Adversarial Network (GAN) based Lung Cancer Classification (LCC) system is developed. The proposed AC-GAN-LCC system consists of three modules; preprocessing, Lungs Region Detection (LRD), and AC-GAN classification. A Wiener filter is employed in the preprocessing module to remove the Gaussian noise. In the LRD module, only the lung regions (left and right lungs) are detected using iterative thresholding and morphological operations. In order to extract the lung region only, flood filling… More >

  • Open Access

    ARTICLE

    Breast Cancer Diagnosis Using Feature Selection Approaches and Bayesian Optimization

    Erkan Akkur1, Fuat TURK2,*, Osman Erogul1

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1017-1031, 2023, DOI:10.32604/csse.2023.033003

    Abstract Breast cancer seriously affects many women. If breast cancer is detected at an early stage, it may be cured. This paper proposes a novel classification model based improved machine learning algorithms for diagnosis of breast cancer at its initial stage. It has been used by combining feature selection and Bayesian optimization approaches to build improved machine learning models. Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Ensemble Learning and Decision Tree approaches were used as machine learning algorithms. All experiments were tested on two different datasets, which are Wisconsin Breast Cancer Dataset (WBCD) and Mammographic Breast Cancer Dataset (MBCD). Experiments were… More >

  • Open Access

    ARTICLE

    Improved Model for Genetic Algorithm-Based Accurate Lung Cancer Segmentation and Classification

    K. Jagadeesh1,*, A. Rajendran2

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

    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 pre-processed with the filters called… More >

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