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  • Open Access

    ARTICLE

    An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

    Sumaia Mohamed Elhassan1, Saad Mohamed Darwish1,*, Saleh Mesbah Elkaffas2

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 835-867, 2025, DOI:10.32604/cmes.2024.058770 - 17 December 2024

    Abstract Lung cancer continues to be a leading cause of cancer-related deaths worldwide, emphasizing the critical need for improved diagnostic techniques. Early detection of lung tumors significantly increases the chances of successful treatment and survival. However, current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue. Single-model deep learning technologies for lung cancer detection, while beneficial, cannot capture the full range of features present in medical imaging data, leading to incomplete or inaccurate detection. Furthermore, it may not be robust enough to handle the… More >

  • Open Access

    ARTICLE

    Multi-Class Skin Cancer Detection Using Fusion of Textural Features Based CAD Tool

    Khushmeen Kaur Brar1, Bhawna Goyal1, Ayush Dogra2, Sampangi Rama Reddy3, Ahmed Alkhayyat4, Rajesh Singh5, Manob Jyoti Saikia6,7,*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4217-4263, 2024, DOI:10.32604/cmc.2024.052548 - 19 December 2024

    Abstract Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade. The diagnosis of skin cancer is of paramount importance, yet the process is intricate and challenging. The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature, the visibility of dense hair, and the presence of various substitute characteristics. The texture of the epidermis is notably different from that of normal skin, and these differences are often evident in cases of unhealthy skin. As a consequence, the development of an effective… More >

  • Open Access

    ARTICLE

    Enhancing Early Detection of Lung Cancer through Advanced Image Processing Techniques and Deep Learning Architectures for CT Scans

    Nahed Tawfik1,*, Heba M. Emara2, Walid El-Shafai3, Naglaa F. Soliman4, Abeer D. Algarni4, Fathi E. Abd El-Samie4

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 271-307, 2024, DOI:10.32604/cmc.2024.052404 - 15 October 2024

    Abstract Lung cancer remains a major concern in modern oncology due to its high mortality rates and multifaceted origins, including hereditary factors and various clinical changes. It stands as the deadliest type of cancer and a significant cause of cancer-related deaths globally. Early diagnosis enables healthcare providers to administer appropriate treatment measures promptly and accurately, leading to improved prognosis and higher survival rates. The significant increase in both the incidence and mortality rates of lung cancer, particularly its ranking as the second most prevalent cancer among women worldwide, underscores the need for comprehensive research into efficient… More >

  • Open Access

    ARTICLE

    Integrating Transformer and Bidirectional Long Short-Term Memory for Intelligent Breast Cancer Detection from Histopathology Biopsy Images

    Prasanalakshmi Balaji1,*, 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 >

  • Open Access

    RETRACTION

    Retraction: Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

    Computer Systems Science and Engineering Editorial Office

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1083-1083, 2024, DOI:10.32604/csse.2024.054265 - 17 July 2024

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Machine Learning Techniques Using Deep Instinctive Encoder-Based Feature Extraction for Optimized Breast Cancer Detection

    Vaishnawi Priyadarshni1, Sanjay Kumar Sharma1, Mohammad Khalid Imam Rahmani2,*, Baijnath Kaushik3, Rania Almajalid2,*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2441-2468, 2024, DOI:10.32604/cmc.2024.044963 - 27 February 2024

    Abstract Breast cancer (BC) is one of the leading causes of death among women worldwide, as it has emerged as the most commonly diagnosed malignancy in women. Early detection and effective treatment of BC can help save women’s lives. Developing an efficient technology-based detection system can lead to non-destructive and preliminary cancer detection techniques. This paper proposes a comprehensive framework that can effectively diagnose cancerous cells from benign cells using the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM) data set. The novelty of the proposed framework lies in the integration of More >

  • Open Access

    ARTICLE

    Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

    Nure Alam Chowdhury1, Lulu Wang2,*, Md Shazzadul Islam3, Linxia Gu1, Mehmet Kaya1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1301-1322, 2024, DOI:10.32604/cmes.2023.030844 - 17 November 2023

    Abstract This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumor cells inside the human breast. The size of the current antenna is small enough (18 mm × 21 mm × 1.6 mm) to distribute around the breast phantom. The operating frequency has been observed from 6–14 GHz with a minimum return loss of −61.18 dB and the maximum gain of current proposed antenna is 5.8 dBi which is flexible with respect to the size of antenna. After the distribution of eight antennas around the breast phantom, the return loss curves were observed in the presence and More > Graphic Abstract

    Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

  • Open Access

    ARTICLE

    An Improved Fully Automated Breast Cancer Detection and Classification System

    Tawfeeq Shawly1, Ahmed A. Alsheikhy2,*

    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 >

  • Open Access

    ARTICLE

    Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

    Sindhuja Manickavasagam*, Poonkuzhali Sugumaran

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 135-151, 2023, DOI:10.32604/csse.2023.030344 - 26 May 2023

    Abstract Cancer disease is a deadliest disease cause more dangerous one. By identifying the disease through Artificial intelligence to getting the mage features directly from patients. This paper presents the lung knob division and disease characterization by proposing an enhancement calculation. Most of the machine learning techniques failed to observe the feature dimensions leads inaccuracy in feature selection and classification. This cause inaccuracy in sensitivity and specificity rate to reduce the identification accuracy. To resolve this problem, to propose a Chicken Sine Cosine Algorithm based Deep Belief Network to identify the disease factor. The general technique… More >

  • Open Access

    ARTICLE

    A Transfer Learning Approach Based on Ultrasound Images for Liver Cancer Detection

    Murtada K. Elbashir1, Alshimaa Mahmoud2, Ayman Mohamed Mostafa1,*, Eslam Hamouda1, Meshrif Alruily1, Sadeem M. Alotaibi1, Hosameldeen Shabana3,4, Mohamed Ezz1,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5105-5121, 2023, DOI:10.32604/cmc.2023.037728 - 29 April 2023

    Abstract The convolutional neural network (CNN) is one of the main algorithms that is applied to deep transfer learning for classifying two essential types of liver lesions; Hemangioma and hepatocellular carcinoma (HCC). Ultrasound images, which are commonly available and have low cost and low risk compared to computerized tomography (CT) scan images, will be used as input for the model. A total of 350 ultrasound images belonging to 59 patients are used. The number of images with HCC is 202 and 148, respectively. These images were collected from ultrasound cases.info (28 Hemangiomas patients and 11 HCC… More >

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