Amjad Rehman¹, Muhammad Mujahid¹, Robertas Damasevicius^2,*, Faten S Alamri³, Tanzila Saba¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.11, pp. 1829-1829, 2024, DOI:10.32604/or.2024.056904 - 16 October 2024

Abstract This article has no abstract. More >

Prasanalakshmi Balaji^1,*, Omar Alqahtani¹, Sangita Babu², Mousmi Ajay Chaurasia³, Shanmugapriya Prakasam⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 443-458, 2024, DOI:10.32604/cmes.2024.053158 - 20 August 2024

Abstract Breast cancer is a significant threat to the global population, affecting not only women but also a threat to the entire population. With recent advancements in digital pathology, Eosin and hematoxylin images provide enhanced clarity in examining microscopic features of breast tissues based on their staining properties. Early cancer detection facilitates the quickening of the therapeutic process, thereby increasing survival rates. The analysis made by medical professionals, especially pathologists, is time-consuming and challenging, and there arises a need for automated breast cancer detection systems. The upcoming artificial intelligence platforms, especially deep learning models, play an More >

Kun Yang^1,2,3, Shilong Chang¹, Yucheng Wang¹, Minghui Wang¹, Jiahui Yang¹, Shuang Liu^1,2,3, Kun Liu^1,2,3, Linyan Xue^1,2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 393-410, 2024, DOI:10.32604/cmc.2023.044994 - 30 January 2024

Abstract Clear cell renal cell carcinoma (ccRCC) represents the most frequent form of renal cell carcinoma (RCC), and accurate International Society of Urological Pathology (ISUP) grading is crucial for prognosis and treatment selection. This study presents a new deep network called Multi-scale Fusion Network (MsfNet), which aims to enhance the automatic ISUP grade of ccRCC with digital histopathology pathology images. The MsfNet overcomes the limitations of traditional ResNet50 by multi-scale information fusion and dynamic allocation of channel quantity. The model was trained and tested using 90 Hematoxylin and Eosin (H&E) stained whole slide images (WSIs), which… More >

Manas Ranjan Prusty¹, Rishi Dinesh², Hariket Sukesh Kumar Sheth², Alapati Lakshmi Viswanath², Sandeep Kumar Satapathy^2,3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718 - 26 December 2023

Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time… More >

Arslan Akram^1,2, Javed Rashid^2,3,4, Fahima Hajjej⁵, Sobia Yaqoob^1,6, Muhammad Hamid⁷, Asma Irshad⁸, Nadeem Sarwar^9,*

CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1081-1101, 2023, DOI:10.32604/cmc.2023.041558 - 31 October 2023

Abstract Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this… More >

Tawfeeq Shawly¹, Ahmed A. Alsheikhy^2,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 731-751, 2023, DOI:10.32604/cmc.2023.039433 - 08 June 2023

Abstract More than 500,000 patients are diagnosed with breast cancer annually. Authorities worldwide reported a death rate of 11.6% in 2018. Breast tumors are considered a fatal disease and primarily affect middle-aged women. Various approaches to identify and classify the disease using different technologies, such as deep learning and image segmentation, have been developed. Some of these methods reach 99% accuracy. However, boosting accuracy remains highly important as patients’ lives depend on early diagnosis and specified treatment plans. This paper presents a fully computerized method to detect and categorize tumor masses in the breast using two… More >

LAKSHANA SADU^#, R.HARI KRISHNAN^#, R.L. AKSHAYA, I. SARANYA, UDIPT RANJAN DAS, SNEHA SATISHKUMAR, N. SELVAMURUGAN^*

BIOCELL, Vol.47, No.4, pp. 751-760, 2023, DOI:10.32604/biocell.2023.026911 - 08 March 2023

Abstract MicroRNAs (miRNAs) are small non-coding RNAs (ncRNAs) that regulate the expression of their target mRNAs post-transcriptionally. Since their discovery, thousands of highly conserved miRNAs have been identified and investigated for their role in human health and diseases. MiR-214 has been increasingly reported to have an association with the regulation of bone metabolism. Reports suggested that miR-214 controls the critical aspects of osteoblasts (bone-forming cells), including their differentiation, proliferation, viability, and migration. Studies have also reported the functional significance of miR-214 in bone diseases and suggested its candidature as a diagnostic and therapeutic target. Further, targeting More >

Muhammad Junaid Umer¹, Muhammad Sharif¹, Majed Alhaisoni², Usman Tariq³, Ye Jin Kim⁴, Byoungchol Chang^5,*

Computer Systems Science and Engineering, Vol.45, No.2, pp. 1001-1016, 2023, DOI:10.32604/csse.2023.030463 - 03 November 2022

Abstract Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work… More >

Ali G. Alkhathami^*

Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 235-247, 2022, DOI:10.32604/iasc.2022.024952 - 15 April 2022

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… More >