Nishant Behar^*, Manish Shrivastava

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3371-3389, 2023, DOI:10.32604/cmc.2023.031949 - 31 October 2022

Abstract Breast cancer is the most prevalent cancer among women, and diagnosing it early is vital for successful treatment. The examination of images captured during biopsies plays an important role in determining whether a patient has cancer or not. However, the stochastic patterns, varying intensities of colors, and the large sizes of these images make it challenging to identify and mark malignant regions in them. Against this backdrop, this study proposes an approach to the pixel categorization based on the genetic algorithm (GA) and principal component analysis (PCA). The spatial features of the images were extracted… 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 - 01 June 2022

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… 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 - 01 June 2022

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… More >

Ali Hanafiah Rambe¹, Muzammil Jusoh^2,3, Samir Salem Al-Bawri^4,5,*, Mahmoud A. Abdelghany^6,7

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5575-5587, 2022, DOI:10.32604/cmc.2022.030902 - 28 July 2022

Abstract This paper proposed integrating the communication system on the garment, which can be utilized to detect breast cancer at an early stage by using an ultra-wideband (UWB) wearable antenna. Breast cancer is an abnormal cell that is located in the breast tissue. Early detection of breast cancer plays an important role, and it helps in the long term for all women. The proposed UWB wearable antenna successfully operates at 3.1–10.6 GHz under an acceptable reflection coefficient of −10 dB. The fabricated wearable antenna was made from Shieldit Super and felt both conductive and nonconductive wearable More >

Areej A. Malibari¹, Marwa Obayya², Mohamed K. Nour³, Amal S. Mehanna⁴, Manar Ahmed Hamza^5,*, Abu Sarwar Zamani⁵, Ishfaq Yaseen⁵, Abdelwahed Motwakel⁵

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4123-4138, 2022, DOI:10.32604/cmc.2022.030492 - 16 June 2022

Abstract With the rapid increase of new cases with an increased mortality rate, cancer is considered the second and most deadly disease globally. Breast cancer is the most widely affected cancer worldwide, with an increased death rate percentage. Due to radiologists’ processing of mammogram images, many computer-aided diagnoses have been developed to detect breast cancer. Early detection of breast cancer will reduce the death rate worldwide. The early diagnosis of breast cancer using the developed computer-aided diagnosis (CAD) systems still needed to be enhanced by incorporating innovative deep learning technologies to improve the accuracy and sensitivity… 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 - 18 May 2022

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.… 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 - 21 April 2022

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… 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 - 15 April 2022

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… More >

Sharifa Khalid Alduraibi^*

Intelligent Automation & Soft Computing, Vol.33, No.2, pp. 1321-1334, 2022, DOI:10.32604/iasc.2022.025021 - 08 February 2022

Abstract This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast cancer between malignant lesions and benign lesions. First, various pre-trained convolutional neural networks are used to calculate the features of breast ultrasonography (BU) images. Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network. After extensive training and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy. Furthermore, the best BU features are merged to increase… More >

Ritam Sharma¹, Janki Ballabh Sharma¹, Ranjan Maheshwari¹, Praveen Agarwal^2,3,4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 923-947, 2022, DOI:10.32604/cmes.2022.016065 - 13 December 2021

Abstract In this paper, a novel hybrid texture feature set and fractional derivative filter-based breast cancer detection model is introduced. This paper also introduces the application of a histogram of linear bipolar pattern features (HLBP) for breast thermogram classification. Initially, breast tissues are separated by masking operation and filtered by Grmwald–Letnikov fractional derivative-based Sobel mask to enhance the texture and rectify the noise. A novel hybrid feature set using HLBP and other statistical feature sets is derived and reduced by principal component analysis. Radial basis function kernel-based support vector machine is employed for detecting the abnormality… More >