Saeed Iqbal^1,*, Adnan N. Qureshi¹, Ghulam Mustafa²

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4915-4939, 2022, DOI:10.32604/cmc.2022.023178

Abstract Skin cancer (melanoma) is one of the most aggressive of the cancers and the prevalence has significantly increased due to increased exposure to ultraviolet radiation. Therefore, timely detection and management of the lesion is a critical consideration in order to improve lifestyle and reduce mortality. To this end, we have designed, implemented and analyzed a hybrid approach entailing convolutional neural networks (CNN) and local binary patterns (LBP). The experiments have been performed on publicly accessible datasets ISIC 2017, 2018 and 2019 (HAM10000) with data augmentation for in-distribution generalization. As a novel contribution, the CNN architecture is enhanced with an intelligible… More >

Amina Bibi¹, Muhamamd Attique Khan¹, Muhammad Younus Javed¹, Usman Tariq², Byeong-Gwon Kang³, Yunyoung Nam^3,*, Reham R. Mostafa⁴, Rasha H. Sakr⁵

CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 2477-2495, 2022, DOI:10.32604/cmc.2022.018917

Abstract Background: In medical image analysis, the diagnosis of skin lesions remains a challenging task. Skin lesion is a common type of skin cancer that exists worldwide. Dermoscopy is one of the latest technologies used for the diagnosis of skin cancer. Challenges: Many computerized methods have been introduced in the literature to classify skin cancers. However, challenges remain such as imbalanced datasets, low contrast lesions, and the extraction of irrelevant or redundant features. Proposed Work: In this study, a new technique is proposed based on the conventional and deep learning framework. The proposed framework consists of two major tasks: lesion segmentation… More >

Reham Alabduljabbar^*, Hala Alshamlan

CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 831-847, 2021, DOI:10.32604/cmc.2021.018402

Abstract The worldwide mortality rate due to cancer is second only to cardiovascular diseases. The discovery of image processing, latest artificial intelligence techniques, and upcoming algorithms can be used to effectively diagnose and prognose cancer faster and reduce the mortality rate. Efficiently applying these latest techniques has increased the survival chances during recent years. The research community is making significant continuous progress in developing automated tools to assist dermatologists in decision making. The datasets used for the experimentation and analysis are ISBI 2016, ISBI 2017, and HAM 10000. In this work pertained models are used to extract the efficient feature. The… More >

Muhammad Attique Khan¹, Tallha Akram², Muhammad Sharif¹, Seifedine Kadry³, Yunyoung Nam^4,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1041-1064, 2021, DOI:10.32604/cmc.2021.016307

Abstract In this work, we propose a new, fully automated system for multiclass skin lesion localization and classification using deep learning. The main challenge is to address the problem of imbalanced data classes, found in HAM10000, ISBI2018, and ISBI2019 datasets. Initially, we consider a pre-trained deep neural network model, DarkeNet19, and fine-tune the parameters of third convolutional layer to generate the image gradients. All the visualized images are fused using a High-Frequency approach along with Multilayered Feed-Forward Neural Network (HFaFFNN). The resultant image is further enhanced by employing a log-opening based activation function to generate a localized binary image. Later, two… More >

Sameh Abd ElGhany^1,2, Mai Ramadan Ibraheem³, Madallah Alruwaili⁴, Mohammed Elmogy^5,*

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 117-135, 2021, DOI:10.32604/cmc.2021.016102

Abstract With the massive success of deep networks, there have been significant efforts to analyze cancer diseases, especially skin cancer. For this purpose, this work investigates the capability of deep networks in diagnosing a variety of dermoscopic lesion images. This paper aims to develop and fine-tune a deep learning architecture to diagnose different skin cancer grades based on dermatoscopic images. Fine-tuning is a powerful method to obtain enhanced classification results by the customized pre-trained network. Regularization, batch normalization, and hyperparameter optimization are performed for fine-tuning the proposed deep network. The proposed fine-tuned ResNet50 model successfully classified 7-respective classes of dermoscopic lesions… More >

R. Thamizhamuthu^*, D. Manjula

CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1147-1160, 2021, DOI:10.32604/cmc.2021.015503

Abstract The deadliest type of skin cancer is malignant melanoma. The diagnosis requires at the earliest to reduce the mortality rate. In this study, an efficient Skin Melanoma Classification (SMC) system is presented using dermoscopic images as a non-invasive procedure. The SMC system consists of four modules; segmentation, feature extraction, feature reduction and finally classification. In the first module, k-means clustering is applied to cluster the colour information of dermoscopic images. The second module extracts meaningful and useful descriptors based on the statistics of local property, parameters of Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model of wavelet and spatial patterns by Dominant… More >

V. Vidya Lakshmi^1,*, J. S. Leena Jasmine²

Computer Systems Science and Engineering, Vol.37, No.2, pp. 233-245, 2021, DOI:10.32604/csse.2021.015700

Abstract Melanoma or skin cancer is the most dangerous and deadliest disease. As the incidence and mortality rate of skin cancer increases worldwide, an automated skin cancer detection/classification system is required for early detection and prevention of skin cancer. In this study, a Hybrid Artificial Intelligence Model (HAIM) is designed for skin cancer classification. It uses diverse multi-directional representation systems for feature extraction and an efficient Exponentially Weighted and Heaped Multi-Layer Perceptron (EWHMLP) for the classification. Though the wavelet transform is a powerful tool for signal and image processing, it is unable to detect the intermediate dimensional structures of a medical… More >