Hanifah Rahmi Fajrin^1,2, Se Dong Min^1,3,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.072609 - 12 January 2026

Abstract Accurate segmentation of breast cancer in mammogram images plays a critical role in early diagnosis and treatment planning. As research in this domain continues to expand, various segmentation techniques have been proposed across classical image processing, machine learning (ML), deep learning (DL), and hybrid/ensemble models. This study conducts a systematic literature review using the PRISMA methodology, analyzing 57 selected articles to explore how these methods have evolved and been applied. The review highlights the strengths and limitations of each approach, identifies commonly used public datasets, and observes emerging trends in model integration and clinical relevance. More >

Chee Chin Lim^1,2,*, Hui Wen Tiu¹, Qi Wei Oung^1,3, Chiew Chea Lau⁴, Xiao Jian Tan^2,5

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070468 - 12 January 2026

Abstract Breast cancer remains one of the leading causes of cancer mortality world-wide, with accurate molecular subtyping is critical for guiding treatment and improving patient outcomes. Traditional molecular subtyping via immuno-histochemistry (IHC) test is invasive, time-consuming, and may not fully represent tumor heterogeneity. This study proposes a non-invasive approach using digital mammography images and deep learning algorithm for classifying breast cancer molecular subtypes. Four pretrained models, including two Convolutional Neural Networks (MobileNet_V3_Large and VGG-16) and two Vision Transformers (ViT_B_16 and ViT_Base_Patch16_Clip_224) were fine-tuned to classify images into HER2-enriched, Luminal, Normal-like, and Triple Negative subtypes. Hyperparameter tuning,… More >

Sannasi Chakravarthy¹, Bharanidharan Nagarajan², Surbhi Bhatia Khan^3,7,*, Vinoth Kumar Venkatesan², Mahesh Thyluru Ramakrishna⁴, Ahlam Al Musharraf⁵, Khursheed Aurungzeb⁶

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 5029-5045, 2024, DOI:10.32604/cmc.2024.052531 - 12 September 2024

Abstract Breast cancer is a type of cancer responsible for higher mortality rates among women. The cruelty of breast cancer always requires a promising approach for its earlier detection. In light of this, the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast tumors. In addition, the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input mammogram. Accordingly, the work proposed an EfficientNet-B0 having a Spatial Attention Layer with More >

Afnan M. Alhassan^*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427 - 27 February 2024

Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has… More >

V. Swetha^*, G. Vadivu

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 707-726, 2023, DOI:10.32604/iasc.2023.035483 - 29 April 2023

Abstract Every year, the number of women affected by breast tumors is increasing worldwide. Hence, detecting and segmenting the cancer regions in mammogram images is important to prevent death in women patients due to breast cancer. The conventional methods obtained low sensitivity and specificity with cancer region segmentation accuracy. The high-resolution standard mammogram images were supported by conventional methods as one of the main drawbacks. The conventional methods mostly segmented the cancer regions in mammogram images concerning their exterior pixel boundaries. These drawbacks are resolved by the proposed cancer region detection methods stated in this paper.… More >

Manar Ahmed Hamza^*

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719 - 15 March 2023

Abstract Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the… More >

P. Malathi^1,*, G. Charlyn Pushpa Latha²

Computer Systems Science and Engineering, Vol.46, No.2, pp. 1741-1758, 2023, DOI:10.32604/csse.2023.035185 - 09 February 2023

Abstract Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN… More >

P. Gomathi^1,*, C. Muniraj², P. S. Periasamy³

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1887-1899, 2023, DOI:10.32604/csse.2023.028808 - 03 November 2022

Abstract The mortality rate decreases as the early detection of Breast Cancer (BC) methods are emerging very fast, and when the starting stage of BC is detected, it is curable. The early detection of the disease depends on the image processing techniques, and it is used to identify the disease easily and accurately, especially the micro calcifications are visible on mammography when they are 0.1 mm or bigger, and cancer cells are about 0.03 mm, which is crucial for identifying in the BC area. To achieve this micro calcification in the BC images, it is necessary… More >

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 - 28 July 2022

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… More >

Yassir Edrees Almalki¹, Maida Khalid², Sharifa Khalid Alduraibi³, Qudsia Yousaf², Maryam Zaffar², Shoayea Mohessen Almutiri⁴, Muhammad Irfan⁵, Mohammad Abd Alkhalik Basha⁶, Alaa Khalid Alduraibi³, Abdulrahman Manaa Alamri⁷, Khalaf Alshamrani⁸, Hassan A. Alshamrani^8,*

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4103-4121, 2022, DOI:10.32604/cmc.2022.029039 - 16 June 2022

Abstract Since reporting cases of breast cancer are on the rise all over the world. Especially in regions such as Pakistan, Saudi Arabia, and the United States. Efficient methods for the early detection and diagnosis of breast cancer are needed. The usual diagnosis procedures followed by physicians has been updated with modern diagnostic approaches that include computer-aided support for better accuracy. Machine learning based practices has increased the accuracy and efficiency of medical diagnosis, which has helped save lives of many patients. There is much research in the field of medical imaging diagnostics that can be… More >