MARCO INVERNIZZI^1,2,*, NICOLA FUSCO^3,4,*

BIOCELL, Vol.46, No.10, pp. 2221-2223, 2022, DOI:10.32604/biocell.2022.021043

Abstract This article has no abstract. More >

D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, P. Vishnu Raja⁴, Dilip Kumar Sharma⁵

Computer Systems Science and Engineering, Vol.44, No.1, pp. 595-612, 2023, DOI:10.32604/csse.2023.025611

Abstract The most salient argument that needs to be addressed universally is Early Breast Cancer Detection (EBCD), which helps people live longer lives. The Computer-Aided Detection (CADs)/Computer-Aided Diagnosis (CADx) system is indeed a software automation tool developed to assist the health professions in Breast Cancer Detection and Diagnosis (BCDD) and minimise mortality by the use of medical histopathological image classification in much less time. This paper purposes of examining the accuracy of the Convolutional Neural Network (CNN), which can be used to perceive breast malignancies for initial breast cancer detection to determine which strategy is efficient for the early identification of… More >

K. Nagalakshmi^1,*, S. Dr. Suriya²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 501-517, 2023, DOI:10.32604/csse.2023.022687

Abstract Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer… More >

Doaa Sami Khafaga¹, Amel Ali Alhussan^1,*, El-Sayed M. El-kenawy^2,3, Ali E. Takieldeen³, Tarek M. Hassan⁴, Ehab A. Hegazy⁵, Elsayed Abdel Fattah Eid⁶, Abdelhameed Ibrahim⁷, Abdelaziz A. Abdelhamid^8,9

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 749-765, 2022, DOI:10.32604/cmc.2022.029605

Abstract One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the breast cancer cases, a set… More >

Shalermchon Tangwachirapan, Wanwisa Thaiwirot^*, Prayoot Akkaraekthalin

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 411-431, 2022, DOI:10.32604/cmc.2022.028294

Abstract This paper presents the design and analysis of antipodal Vivaldi antennas (AVAs) for breast cancer detection. In order to enhance the antenna gain, different techniques such as using the uniform and non-uniform corrugation, expanding the dielectric substrate and adding the parasitic patch are applied to original AVA. The design procedure of two developed AVA structures i.e., AVA with non-uniform corrugation and AVA with parasitic patch are presented. The proposed AVAs are designed on inexpensive FR4 substrate. The AVA with non-uniform corrugation has compact dimension of mm² or , where is wavelength of the lowest operating frequency. The antenna can operate… More >

P. Indra, G. Kavithaa^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 877-893, 2022, DOI:10.32604/iasc.2022.025982

Abstract Image processing innovations assume a significant part in diagnosing and distinguishing diseases and monitoring these diseases’ quality. In Medical Images, detection of breast cancer in its earlier stage is most important in this field. Because of the low contrast and uncertain design of the tumor cells in breast images, it is still challenging to classify breast tumors only by visual testing by the radiologists. Hence, improvement of computer-supported strategies has been introduced for breast cancer identification. This work presents an efficient computer-assisted method for breast cancer classification of digital mammograms using Adaptive Resource Allocation Network (ARAN). At first, breast cancer… More >

Yassir Edrees Almalki1¹, Ahmad Shaf², Tariq Ali², Muhammad Aamir², Sharifa Khalid Alduraibi³, Shoayea Mohessen Almutiri⁴, Muhammad Irfan⁵, Mohammad Abd Alkhalik Basha⁶, Alaa Khalid Alduraibi³, Abdulrahman Manaa Alamri⁷, Muhammad Zeeshan Azam⁸, Khalaf Alshamrani^9,*, Hassan A. Alshamrani⁹

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4833-4851, 2022, DOI:10.32604/cmc.2022.027111

Abstract Breast cancer (BC) is the most common cause of women’s deaths worldwide. The mammography technique is the most important modality for the detection of BC. To detect abnormalities in mammographic images, the Breast Imaging Reporting and Data System (BI-RADs) is used as a baseline. The correct allocation of BI-RADs categories for mammographic images is always an interesting task, even for specialists. In this work, to detect and classify the mammogram images in BI-RADs, a novel hybrid model is presented using a convolutional neural network (CNN) with the integration of a support vector machine (SVM). The dataset used in this research… More >

D. Banumathy^1,*, Osamah Ibrahim Khalaf², Carlos Andrés Tavera Romero³, J. Indra⁴, Dilip Kumar Sharma⁵

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 667-685, 2022, DOI:10.32604/iasc.2022.025609

Abstract Lump in the breast, discharge of blood from the nipple, and deformation of the nipple/breast and its texture are the symptoms of breast cancer. Though breast cancer is very common in women, men can also get breast cancer. In the early stages, BCD makes use of Thermal Mammograms Breast Images (TMBI). The cost of treatment can be severely reduced in the early stages of detection. Based on the techniques of segmentation, the Breast Cancer Detection (BCD) works. Moreover, by providing a balanced, reliable and appropriate second opinion, a tremendous role has been played by ML in medical practices due to… More >

Ali G. Alkhathami^*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 235-247, 2022, DOI:10.32604/iasc.2022.024952

Abstract Breast cancer is one of the main causes of death in women. When body tissues start behaves abnormally and the ratio of tissues growth becomes asymmetrical then this stage is called cancer. Invasive ductal carcinoma (IDC) is the early stage of breast cancer. The early detection and diagnosis of invasive ductal carcinoma is a significant step for the cure of IDC breast cancer. This paper presents a convolutional neural network (CNN) approach to detect and visualize the IDC tissues in breast on histological images dataset. The dataset consists of 90 thousand histopathological images containing two categories: Invasive Ductal Carcinoma positive… More >

Jiayi Huang¹, Xue Bai¹, Xiaofeng Xie¹, Liping Chen¹, Xiaofeng Lan¹, Qiuyi Zhang¹, Lin Song¹, Pengjiao Hong^2,3, Caiwen Du^1,*

Oncologie, Vol.24, No.1, pp. 131-145, 2022, DOI:10.32604/oncologie.2022.019881

Abstract Background: Vinorelbine can be used to treat metastatic breast cancer as a single agent or in combination with other chemotherapy agents, although there is little real-world data for its use, particularly the oral form, in China. The current study aimed to explore the efficacy and safety of oral vinorelbine in patients with metastatic breast cancer in real-world clinical practice. Methods: A total of 194 patients with metastatic breast cancer received oral vinorelbine as a treatment between February 2017 and January 2021 at the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences… More >