Nishant Behar^*, Manish Shrivastava

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 823-839, 2022, DOI:10.32604/cmes.2022.017030

Abstract Breast cancer is considered an immense threat and one of the leading causes of mortality in females. It is curable only when detected at an early stage. A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images. Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images. Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection. Nowadays, computer-based automated systems play an important role in cancer image analysis and help healthcare experts make rapid and correct inferences about the… 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

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 in the thermogram. The performance… More >

Shakra Mehak¹, M. Usman Ashraf², Rabia Zafar³, Ahmed M. Alghamdi⁴, Ahmed S. Alfakeeh⁵, Fawaz Alassery⁶, Habib Hamam⁷, Muhammad Shafiq^8,*

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3407-3423, 2022, DOI:10.32604/cmc.2022.022705

Abstract Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but nuclear atypia in breast cancer… More >

Jing Lu¹, Yan Wu^2,#, Mingyan Hu¹, Yao Xiong¹, Yapeng Zhou¹, Ziliang Zhao¹, Liutong Shang^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.1, pp. 365-377, 2022, DOI:10.32604/cmes.2021.017897

Abstract Background: The main cause of breast cancer is the deterioration of malignant tumor cells in breast tissue. Early diagnosis of tumors has become the most effective way to prevent breast cancer. Method: For distinguishing between tumor and non-tumor in MRI, a new type of computer-aided detection CAD system for breast tumors is designed in this paper. The CAD system was constructed using three networks, namely, the VGG16, Inception V3, and ResNet50. Then, the influence of the convolutional neural network second migration on the experimental results was further explored in the VGG16 system. Result: CAD system built based on VGG16, Inception… More >

LIPING WANG^1,2, ZHOU LUO¹, MINMIN SUN³, QIUYUE YUAN⁴, YINGGANG ZOU⁵, DEYUAN FU^1,*

BIOCELL, Vol.46, No.3, pp. 595-606, 2022, DOI:10.32604/biocell.2022.017337

Abstract This work aimed to improve current prognostic signatures based on clinical stages in identifying high-risk patients of triple-negative breast cancer (TNBC), to allow patients with a high-risk score for specific treatment decisions. In this study, 396 TNBC samples from TCGA and GEO databases were included in genome-wide transcriptome analysis. The relationship between normalized gene expression values and survival data of patients was determined by Cox proportional hazards models in each dataset. The overlapped genes among all datasets were considered as a potential prognostic signature. The risk score was constructed based on individual genes and validated with three separate data sets… More >

RAGHDA A. SOLIMAN¹, RANA A. YOUNESS^1,2,*, TAMER M. MANIE³, EMAD KHALLAF⁴, MOHAMED EL-SHAZLY¹, MONA ABDELMOHSEN⁵, HEBA HANDOUSSA¹, MOHAMED Z. GAD^6,*

BIOCELL, Vol.46, No.3, pp. 769-783, 2022, DOI:10.32604/biocell.2022.016636

Abstract Triple Negative Breast Cancer (TNBC) immunotherapy has recently shown promising approach. However, some TNBC patients presented with resistance. One of the reasons was attributed to the excessive release of cytokines at the tumor microenvironment (TME) such as Tumor necrosis factor alpha (TNF-α) and Interleukin-10 (IL-10). Fine regulation of these cytokines’ levels via non-coding RNAs (ncRNAs) might alleviate the immune quiescent nature of TME at TNBC tumors. However, the extrapolation of ncRNAs as therapeutic tools is highly challenging. Therefore, disentanglement the nature for the isolation of natural compounds that could modulate the ncRNAs and their respective targets is an applicable translational… More >

R. Rajakumari^1,*, L. Kalaivani²

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1089-1107, 2022, DOI:10.32604/iasc.2022.020178

Abstract Breast cancer is a commonly diagnosed disease in women. Early detection, a personalized treatment approach, and better understanding are necessary for cancer patients to survive. In this work, a deep learning network and traditional convolution network were both employed with the Digital Database for Screening Mammography (DDSM) dataset. Breast cancer images were subjected to background removal followed by Wiener filtering and a contrast limited histogram equalization (CLAHE) filter for image restoration. Wavelet packet decomposition (WPD) using the Daubechies wavelet level 3 (db3) was employed to improve the smoothness of the images. For breast cancer recognition, these preprocessed images were first… More >

S. Prakash^1,*, M. Vinoth Kumar², R. Saravana Ram³, Miodrag Zivkovic⁴, Nebojsa Bacanin⁴, Milos Antonijevic⁴

Computer Systems Science and Engineering, Vol.41, No.2, pp. 735-749, 2022, DOI:10.32604/csse.2022.020533

Abstract Breast cancer has become the second leading cause of death among women worldwide. In India, a woman is diagnosed with breast cancer every four minutes. There has been no known basis behind it, and detection is extremely challenging among medical scientists and researchers due to unknown reasons. In India, the ratio of women being identified with breast cancer in urban areas is 22:1. Symptoms for this disease are micro calcification, lumps, and masses in mammogram images. These sources are mostly used for early detection. Digital mammography is used for breast cancer detection. In this study, we introduce a new hybrid… More >

PRIYANKA THAKUR¹, REENA V. SAINI^2,3,*, ANIL K. CHHILLAR⁴, NEERAJ K. SAINI⁵, VIJAY KUMAR THAKUR⁶, SAMARJEET SINGH SIWAL⁷, ADESH K. SAINI^2,3,*

BIOCELL, Vol.46, No.2, pp. 309-324, 2022, DOI:10.32604/biocell.2022.016916

Abstract Breast cancer, also recognized as the principal cause of cancer-related deaths among women, is the second most familiar and prevalent form of cancer. New diagnostic and prognostic biomarkers that are highly specific are urgently needed for its early prognosis. MicroRNAs (miRNAs), a class of non-coding RNAs, are known to control the biological processes involving transcription, post-transcriptional and covalent modifications, splicing, translation, cell differentiation, proliferation, apoptosis, cancer progression, and invasion. Any dysregulation in miRNA expression, demonstrating their oncogenic and tumor-suppressive functions, contributes to cancer progression. MicroRNA-21 (miR-21), an ‘onco-miR’ in breast cancer, is involved in tumor progression and metastasis by suppressing… More >

S. Sathiya Devi¹, S. Vidivelli^2,*

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3049-3066, 2022, DOI:10.32604/cmc.2022.019917

Abstract Breast cancer is one of the common invasive cancers and stands at second position for death after lung cancer. The present research work is useful in image processing for characterizing shape and gray-scale complexity. The proposed Modified Differential Box Counting (MDBC) extract Fractal features such as Fractal Dimension (FD), Lacunarity, and Succolarity for shape characterization. In traditional DBC method, the unreasonable results obtained when FD is computed for tumour regions with the same roughness of intensity surface but different gray-levels. The problem is overcome by the proposed MDBC method that uses box over counting and under counting that covers the… More >