Armughan Ali^1,2, Hooria Shahbaz², Robertas Damaševičius^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1367-1398, 2025, DOI:10.32604/cmc.2025.059301 - 26 March 2025

Abstract Skin cancer is the most prevalent cancer globally, primarily due to extensive exposure to Ultraviolet (UV) radiation. Early identification of skin cancer enhances the likelihood of effective treatment, as delays may lead to severe tumor advancement. This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis, with an architecture that integrates a Vision Transformer, a bespoke convolutional neural network (CNN), and an Xception module. They were evaluated using two benchmark datasets, HAM10000 and Skin Cancer ISIC. On the HAM10000, the model achieves a precision of 95.46%, an… More >

Rafid Sagban^1,2,*, Haydar Abdulameer Marhoon^3,4, Saadaldeen Rashid Ahmed^5,6,*

Intelligent Automation & Soft Computing, Vol.39, No.6, pp. 1035-1051, 2024, DOI:10.32604/iasc.2024.058416 - 30 December 2024

Abstract Skin cancer is a highly frequent kind of cancer. Early identification of a phenomenon significantly improves outcomes and mitigates the risk of fatalities. Melanoma, basal, and squamous cell carcinomas are well-recognized cutaneous malignancies. Malignant We can differentiate Melanoma from non-pigmented carcinomas like basal and squamous cell carcinoma. The research on developing automated skin cancer detection systems has primarily focused on pigmented malignant type melanoma. The limited availability of datasets with a wide range of lesion categories has hindered in-depth exploration of non-pigmented malignant skin lesions. The present study investigates the feasibility of automated methods for… More >

Khushmeen Kaur Brar¹, Bhawna Goyal¹, Ayush Dogra², Sampangi Rama Reddy³, Ahmed Alkhayyat⁴, Rajesh Singh⁵, Manob Jyoti Saikia^6,7,*

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 >

Madiha Hameed^1,3, Aneela Zameer^1,*, Muhammad Asif Zahoor Raja²

CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2131-2164, 2024, DOI:10.32604/cmes.2024.050124 - 08 July 2024

Abstract The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection… More >

Syeda Shamaila Zareen^1,*, Guangmin Sun^1,*, Mahwish Kundi², Syed Furqan Qadri³, Salman Qadri⁴

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1497-1519, 2024, DOI:10.32604/cmc.2024.047418 - 25 April 2024

Abstract Skin cancer diagnosis is difficult due to lesion presentation variability. Conventional methods struggle to manually extract features and capture lesions spatial and temporal variations. This study introduces a deep learning-based Convolutional and Recurrent Neural Network (CNN-RNN) model with a ResNet-50 architecture which used as the feature extractor to enhance skin cancer classification. Leveraging synergistic spatial feature extraction and temporal sequence learning, the model demonstrates robust performance on a dataset of 9000 skin lesion photos from nine cancer types. Using pre-trained ResNet-50 for spatial data extraction and Long Short-Term Memory (LSTM) for temporal dependencies, the model More >

Muhammad Suleman¹, Faizan Ullah¹, Ghadah Aldehim^2,*, Dilawar Shah¹, Mohammad Abrar^1,3, Asma Irshad⁴, Sarra Ayouni²

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3533-3549, 2023, DOI:10.32604/cmc.2023.042365 - 26 December 2023

Abstract The early detection of skin cancer, particularly melanoma, presents a substantial risk to human health. This study aims to examine the necessity of implementing efficient early detection systems through the utilization of deep learning techniques. Nevertheless, the existing methods exhibit certain constraints in terms of accessibility, diagnostic precision, data availability, and scalability. To address these obstacles, we put out a lightweight model known as Smart MobiNet, which is derived from MobileNet and incorporates additional distinctive attributes. The model utilizes a multi-scale feature extraction methodology by using various convolutional layers. The ISIC 2019 dataset, sourced from… More >

C. S. S. Anupama¹, Saud Yonbawi², G. Jose Moses³, E. Laxmi Lydia⁴, Seifedine Kadry^5,6,7, Jungeun Kim^8,*

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2079-2095, 2023, DOI:10.32604/csse.2023.038322 - 28 July 2023

Abstract Skin cancer is one of the most dangerous cancer. Because of the high melanoma death rate, skin cancer is divided into non-melanoma and melanoma. The dermatologist finds it difficult to identify skin cancer from dermoscopy images of skin lesions. Sometimes, pathology and biopsy examinations are required for cancer diagnosis. Earlier studies have formulated computer-based systems for detecting skin cancer from skin lesion images. With recent advancements in hardware and software technologies, deep learning (DL) has developed as a potential technique for feature learning. Therefore, this study develops a new sand cat swarm optimization with a… More >

K. Kalyani¹, Sara A Althubiti², Mohammed Altaf Ahmed³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Neunggyu Han⁶, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 149-164, 2023, DOI:10.32604/cmc.2023.033005 - 06 February 2023

Abstract Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification… More >

G. Murugesan^1,*, J. Jeyapriya², M. Hemalatha³, S. Rajeshkannan⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2099-2109, 2023, DOI:10.32604/iasc.2023.032068 - 05 January 2023

Abstract The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of… More >

J. Jaculin Femil^1,*, T. Jaya²

Computer Systems Science and Engineering, Vol.45, No.3, pp. 2919-2934, 2023, DOI:10.32604/csse.2023.032935 - 21 December 2022

Abstract The necessity of on-time cancer detection is extremely high in the recent days as it becomes a threat to human life. The skin cancer is considered as one of the dangerous diseases among other types of cancer since it causes severe health impacts on human beings and hence it is highly mandatory to detect the skin cancer in the early stage for providing adequate treatment. Therefore, an effective image processing approach is employed in this present study for the accurate detection of skin cancer. Initially, the dermoscopy images of skin lesions are retrieved and processed… More >