Ni Ruiwen¹, Mu Ye^1,2,3,4,*, Li Ji¹, Zhang Tong¹, Luo Tianye¹, Feng Ruilong¹, Gong He^1,2,3,4, Hu Tianli^1,2,3,4, Sun Yu^1,2,3,4, Guo Ying^1,2,3,4, Li Shijun^5,6, Thobela Louis Tyasi⁷

CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3263-3274, 2022, DOI:10.32604/cmc.2022.026881

Abstract In order to accurately segment architectural features in high-resolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed. First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building. The remote sensing image and its truth map were used as the input in the U-net network, followed by the addition of the building ground prediction layer at the end of the U-net network. Based on the ResNet network, a multi-task network with the boundary distance prediction layer was built. Experiments involving the ISPRS aerial… More >

A. Soujanya^1,*, N. Nandhagopal²

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 675-687, 2023, DOI:10.32604/iasc.2023.025930

Abstract Due to the rising occurrence of skin cancer and inadequate clinical expertise, it is needed to design Artificial Intelligence (AI) based tools to diagnose skin cancer at an earlier stage. Since massive skin lesion datasets have existed in the literature, the AI-based Deep Learning (DL) models find useful to differentiate benign and malignant skin lesions using dermoscopic images. This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet (ARGS-OEN) technique for skin lesion segmentation and classification. The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process takes place using the Flower… More >

L. K. Shoba^1,*, P. Mohan Kumar²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 613-628, 2023, DOI:10.32604/csse.2023.024449

Abstract Nowadays in the medical field, imaging techniques such as Optical Coherence Tomography (OCT) are mainly used to identify retinal diseases. In this paper, the Central Serous Chorio Retinopathy (CSCR) image is analyzed for various stages and then compares the difference between CSCR before as well as after treatment using different application methods. The first approach, which was focused on image quality, improves medical image accuracy. An enhancement algorithm was implemented to improve the OCT image contrast and denoise purpose called Boosted Anisotropic Diffusion with an Unsharp Masking Filter (BADWUMF). The classifier used here is to figure out whether the OCT… More >

Kingsley A. Ogudo¹, R. Surendran^2,*, Osamah Ibrahim Khalaf³

Computer Systems Science and Engineering, Vol.44, No.1, pp. 693-707, 2023, DOI:10.32604/csse.2023.024154

Abstract Skin lesions have become a critical illness worldwide, and the earlier identification of skin lesions using dermoscopic images can raise the survival rate. Classification of the skin lesion from those dermoscopic images will be a tedious task. The accuracy of the classification of skin lesions is improved by the use of deep learning models. Recently, convolutional neural networks (CNN) have been established in this domain, and their techniques are extremely established for feature extraction, leading to enhanced classification. With this motivation, this study focuses on the design of artificial intelligence (AI) based solutions, particularly deep learning (DL) algorithms, to distinguish… More >

S. Mohan Kumar^*, T. Kumanan

Computer Systems Science and Engineering, Vol.44, No.1, pp. 833-844, 2023, DOI:10.32604/csse.2023.022385

Abstract The main cause of skin cancer is the ultraviolet radiation of the sun. It spreads quickly to other body parts. Thus, early diagnosis is required to decrease the mortality rate due to skin cancer. In this study, an automatic system for Skin Lesion Classification (SLC) using Non-Subsampled Shearlet Transform (NSST) based energy features and Support Vector Machine (SVM) classifier is proposed. At first, the NSST is used for the decomposition of input skin lesion images with different directions like 2, 4, 8 and 16. From the NSST’s sub-bands, energy features are extracted and stored in the feature database for training.… More >

Victoria Gonzáles-Gutierrez¹, Aldo Alvarez-Risco², Alfredo Estrada-Merino², María de las Mercedes Anderson-Seminario², Sabina Mlodzianowska², Shyla Del-Aguila-Arcentales³, Jaime A. Yáñez^4,5,*

International Journal of Mental Health Promotion, Vol.24, No.4, pp. 565-581, 2022, DOI:10.32604/ijmhp.2022.021176

Abstract The current study measures the influence of multitasking behavior and self-efficacy for self-regulated learning (SESRL) on perceptions of academic performance and views in university students during the COVID-19 pandemic in Mexico. 264 university students fulfilled an online questionnaire. It was observed that multitasking behavior negatively influences SESRL (−0.203), while SESRL showed a positive influence of 0.537 on perceptions of academic performance, and multitasking behavior had an influence of −0.097 on the perception of academic performance. Cronbach’s alpha and Average Variance Extracted values were 0.809 and 0.577 (multitasking behavior), 0.819 and 0.626 (SESRL), 0.873 and 0.725 (perceptions of academic performance), respectively.… More >

Romany F. Mansour^1,*, Sara A. Althubiti², Fayadh Alenezi³

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 849-864, 2022, DOI:10.32604/cmc.2022.029265

Abstract Recently, computer vision (CV) based disease diagnosis models have been utilized in various areas of healthcare. At the same time, deep learning (DL) and machine learning (ML) models play a vital role in the healthcare sector for the effectual recognition of diseases using medical imaging tools. This study develops a novel computer vision with optimal machine learning enabled skin lesion detection and classification (CVOML-SLDC) model. The goal of the CVOML-SLDC model is to determine the appropriate class labels for the test dermoscopic images. Primarily, the CVOML-SLDC model derives a gaussian filtering (GF) approach to pre-process the input images and graph… More >

Mohamed W. Abo El-Soud^1,2,*, Tarek Gaber^2,3, Mohamed Tahoun², Abdullah Alourani¹

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1109-1123, 2022, DOI:10.32604/cmc.2022.028561

Abstract The prevalence of melanoma skin cancer has increased in recent decades. The greatest risk from melanoma is its ability to broadly spread throughout the body by means of lymphatic vessels and veins. Thus, the early diagnosis of melanoma is a key factor in improving the prognosis of the disease. Deep learning makes it possible to design and develop intelligent systems that can be used in detecting and classifying skin lesions from visible-light images. Such systems can provide early and accurate diagnoses of melanoma and other types of skin diseases. This paper proposes a new method which can be used for… More >

Fikret Yalcinkaya^*, Ali Erbas

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1961-1977, 2022, DOI:10.32604/iasc.2022.024961

Abstract Early stage melanoma detection (ESMD) is crucial as late detection kills. Computer aided diagnosis systems (CADS) integrated with high level algorithms are major tools capable of ESMD with high degree of accuracy, specificity, and sensitivity. CADS use the image and the information within the pixels of the image. Pixels’ characteristics and orientations determine the colour and shapes of the images as the pixels and associated environment are closely interrelated with the lesion. CADS integrated with Convolutional Neural Networks (CNN) specifically play a major role for ESMD with high degree of accuracy. The proposed system has two steps to produce high… More >

Iqra Abid¹, Sultan Almakdi², Hameedur Rahman³, Ahmed Almulihi⁴, Ali Alqahtani², Khairan Rajab^2,5, Abdulmajeed Alqhatani^2,*, Asadullah Shaikh²

Intelligent Automation & Soft Computing, Vol.33, No.3, pp. 1407-1421, 2022, DOI:10.32604/iasc.2022.023753

Abstract Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided skin cancer diagnosis system is the accurate segmentation of skin malignancy. To achieve this, a specialized skin image analysis technique must be used for the separation of cancerous parts from important healthy skin. This procedure is called Dermatography. Researchers have often used multiple techniques for the analysis of skin images, but, because of their low accuracy, most of these methods have turned out to be at best, inconsistent. Proper clinical treatment involves sensitivity in the surgical… More >