V. Swetha^*, G. Vadivu

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

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. The mammogram images are classified… 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

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 model to diagnose BCa from… 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

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 to focus on the four… 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 >

José Escorcia-Gutierrez^1,*, Romany F. Mansour², Kelvin Beleño³, Javier Jiménez-Cabas⁴, Meglys Pérez¹, Natasha Madera¹, Kevin Velasquez¹

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4221-4235, 2022, DOI:10.32604/cmc.2022.022322

Abstract Biomedical image processing is a hot research topic which helps to majorly assist the disease diagnostic process. At the same time, breast cancer becomes the deadliest disease among women and can be detected by the use of different imaging techniques. Digital mammograms can be used for the earlier identification and diagnostic of breast cancer to minimize the death rate. But the proper identification of breast cancer has mainly relied on the mammography findings and results to increased false positives. For resolving the issues of false positives of breast cancer diagnosis, this paper presents an automated deep learning based breast cancer… More >

Naglaa F. Soliman^1,2, Naglaa S. Ali², Mahmoud I. Aly^2,3, Abeer D. Algarni^1,*, Walid El-Shafai⁴, Fathi E. Abd El-Samie^1,4

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1315-1334, 2022, DOI:10.32604/cmc.2022.017001

Abstract Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main steps: pre-processing, segmentation of the… More >